TalkMine: a Soft Computing Approach to Adaptive Knowledge Recommendation

Complex Systems Modeling
Modeling, Algorithms, and Informatics Group (CCS-3)
Los Alamos National Laboratory, MS B256
Los Alamos, New Mexico 87545, USA

Citation: Rocha, Luis M. [2001]."TalkMine: A Soft Computing Approach to Adaptive Knowledge Recommendation". In: Soft Computing Agents: New Trends for Designing Autonomous Systems. Vincenzo Loia and Salvatore Sessa (Eds.). Series on Studies in Fuzziness and Soft Computing. Physica-Verlag, Springer, pp. 89-116. LAUR-00-4914

The full paper is available in Adobe Acrobat (.pdf) format. Due to mathematical formalisms not suitable to an HTML format, only the first introductory sections (and references) are available here in HTML.


We present a soft computing recommendation system named TalkMine, to advance adaptive web and digital library technology. TalkMine leads different databases or websites to learn new and adapt existing keywords to the categories recognized by its communities of users. It uses distributed artificial intelligence algorithms and soft computing technology. TalkMine is currently being implemented for the research library of the Los Alamos National Laboratory under the Active Recommendation Project

TalkMineis based on the integration of distributed knowledge networks using Evidence Sets, an extension of fuzzy sets. The identification of the interests of users relies on a process of combining several fuzzy sets into evidence sets, which models an ambiguous "and/or" linguistic expression. The interest of users is further fine-tuned by a human-machine conversation algorithm used for uncertainty reduction. Documents are retrieved according to the inferred user interests. Finally, the retrieval behavior of all users of the system is employed to adapt the knowledge bases of queried information resources. This adaptation allows information resources to respond well to the evolving expectations of users.

In this article the distributed architecture of TalkMine is presented together with a description of its implementation in the Active Recommendation Project. In particular, the characterization of information resources as interacting distributed memory banks is presented. Evidence sets and the operations to produce them from several fuzzy sets are detailed. The conversation and adaptation algorithms used by TalkMine to interact automatically with users is described.

Keywords: Recommendation Systems, Information Retrieval, Web-related technologies, Fuzzy Set Theory, Evidence Sets, Measures of Uncertainty, Collaborative Systems, Adaptive Systems, Distributed Artificial Intelligence, Human-machine Interaction, Communities of Agents, Knowledge Representation, Soft Computing.

1 Towards Adaptive Web-Technology using Soft Computing

1.1 Distributed Information Systems and Information Retrieval

Distributed Information Systems (DIS) are collections of electronic networked information resources (e.g. databases) in some kind of interaction with communities of users; examples of such systems are: the Internet, the World Wide Web, corporate intranets, databases, library information retrieval systems, etc. DIS serve large and diverse communities of users by providing access to a large set of heterogeneous electronic information resources. Information Retrieval (IR) refers to all the methods and processes for searching relevant information out of information systems (isolated or part of DIS) that contain extremely large numbers of documents. As the complexity and size of both user communities and information resources grows, the fundamental limitations of traditional information retrieval systems have become evident in modern DIS.

Traditional IR systems are based solely on keywords that index (semantically characterize) documents and a query language to retrieve documents from centralized databases according to these keywords - users need to know how to "pull" relevant information from passive databases. This setup leads to a number of flaws (Rocha and Bollen, 2000), which prevent traditional IR processes in DIS to achieve any kind of interesting coupling with users. The human-machine interaction observed in these systems is particularly rigid: Most cannot pro-actively "push" relevant information to its users about related topics that they may be unaware of, there is typically no mechanism to exchange knowledge, or crossover of relevant information among users and information resources, and there is no mechanism to recombine knowledge in different information resources to infer new linguistic categories of keywords used by evolving communities of users. In other words, traditional IR keeps DIS as static, passive, and isolated repositories of data; no interesting human-machine co-evolution of knowledge or learning is achieved.

1.2 Enabling Evolving DIS with Soft Computing

The limitations of traditional IR and DIS are even more dramatic when contrasted with biological distributed systems such as immune, neural, insect, and social networks. Biological networks function largely in a distributed manner, without recourse to central controllers, while achieving tremendous ability to respond in concerted ways to different environmental necessities. In particular, they are typically endowed with the ability to elicit appropriate responses to specific demands, to transfer and process relevant information across the network, and to adapt to a changing environment by creating novel behaviors (often from recombination of existing ones). These abilities are precisely what has been lacking in IR.

Biological networks effectively evolve in an open-ended manner; we are interested in endowing DIS with a similar open-ended capacity to evolve with their users - to achieve an open-ended semiosis with them (Rocha, 2000). In biology, open-ended evolution originates from the existence of material building blocks that self-organize non-linearly (e.g. Kauffman, 1993) and are combined via a specification control, such as the genetic system (Rocha, 1998). In contrast, computer systems were constructed precisely with rigid building blocks constrained in such a way as to allow minimum dynamic self-organization and maximum programmability, which results in no inherent evolvability (Conrad, 1990). Therefore, to attain any evolvability in current digital computer systems, we need to program in some "softer" building blocks that can be used to realize the kind of dynamical richness we encounter in biological systems (Rocha and Bollen, 2000).

The ultimate goal of IR is to produce or recommend relevant information to users. It seems obvious that the foundation of any useful recommendation should be first and foremost based on the identification of users and subject matter. In this sense, the goal of recommendation systems can be seen as similar to that of most biological systems, in particular immune systems: to recognize agents (users) and elicit appropriate responses from components of the distributed information network. Furthermore, the information network should learn and adapt to the community of agents (users) it interacts with - its environment.

Nevertheless, traditional IR does not identify users and classifies subjects only with unchanging keywords and categories. To build more flexible IR and evolving DIS, we need to design recommendation systems endowed with:

  1. A means to recognize users .
  2. A means to characterize information resources.
  3. A 2-way means to exchange knowledge between users and information resources: a conversation process. As information resources become more and more complex, we cannot expect a simple 1-way query ("pull") to work well. Instead, we need a means to integrate the interests of the user with the knowledge specific to each information resource via an interactive recommendation process ("push").
  4. Adaptation mechanisms. We also want DIS to adapt to their community of users, as well as to exchange and re-combine knowledge leading to evolvability and creativity.

Below I describe efforts to include these design requirements for recommendation systems using Soft Computing technology. I also discuss how a useful and more natural knowledge management of DIS is achieved with these soft computing designs. Let us start with some background on IR and recommendation systems.

1.3 From IR to Active Recommendation: From "Pull" to "Push"

New approaches to IR have been proposed to improve its inflexible algorithms. Active recommendation systems, also known as Active Collaborative Filtering (Chislenko, 1998) or Knowledge Self-Organization (Johnson et al, 1998) are IR systems which rely on active computational environments that interact with and adapt to their users. They effectively "push" relevant information to users according to previous patterns of IR or individual user profiling.

Recommendation systems are typically based on human-machine interaction mediated by intelligent agents, or other decentralized components, and come in several varieties:

  1. In content-based recommendation, user profiles are created based on the system's keywords. Documents are recommended to users according to the similarity of their profiles and the similarity of keywords constructed from a semantic distance function obtained from the associations between keywords and documents. Two documents are close when they are classified by many of the same keywords. This is the case of systems such as InfoFinder (Krulwich and Burkey, 1996), NewsWeeder (Lang, 1995), and many systems developed for the routing task at the TREC Conferences (Harman, 1994).
  2. In collaborative recommendation no description of the semantics or content of documents is involved, rather recommendations are issued according to a comparison of the profiles of several users that tend to access the same documents. The comparison depends on a distance function between user profiles, defined not by keywords, but on the sets of actual documents retrieved. Two user profiles are close when their users have retrieved many of the same documents. This is the case of systems such as GroupLens (Resnick et al, 1994; Kostan et al, 1997), Bellcore Video Recommender (Hill et al, 1995), Ringo ( Shardanad and Maes, 1995). When user feedback is allowed, this type of recommendation is known as Information Filtering (Good et al, 1999). For a description of the collaborative recommendation framework see Herlocker et al (1999).
  3. In structural recommendation, data-mining techniques are employed on the relations among documents and keywords, to discover related documents or documents of particular importance (authorities) in a given information resource. A large portion of work in this area, is concerned with the analysis of the graph structure of Web Hyperlinks (regardless of document keywords), e.g. work pursued under the CLEVER Project (Kleinberg, 1998; Chakrabarti et al, 1999), or other graph-theoretic approaches such as Watts' (1999) Small World graphs. A second large area of research is concerned with the semantic relations between documents and keywords, which are analyzed with algebraic techniques such as Singular Value Decomposition, known in IR as Latent Semantic Indexing (LSI) (Berry et al, 1994; Kannan and Vempala, 1999). Documents are recommended to users according to the way they are associated with other documents and/or keywords: the semantic structure of information resources.
  4. In collective recommendation, the behavior of communities of users is integrated, and utilized to adapt the structure (the pattern of associations) of information resources. This kind of system tracks the paths users follow in the structure of information resources as they retrieve documents. The more certain sets of documents tend to be retrieved together in paths followed by different users, the closer they become in the structure of the information resource. This type of algorithm employs the distributed behavior of a collection of users to adapt DIS, resulting in systems that learn the interests of their communities of users much in the same way as social insects discover paths based on the pheromone trails left behind by other insects in their colony (Rocha and Bollen, 2000), thus, in time, recommending more and more appropriate documents. This is the case of Adaptive Hypertext systems (Brusilovsky et al, 1998; Bollen and Heylighen, 1998; Eklund, 1998), Knowledge Self-Organization (Johnson et al, 1998; Heylighen, 1999), as well as the work on the collective discovery of linguistic categories (Rocha, 1997a, 2000) detailed below.

Content-based systems depend on single user profiles, and thus cannot effectively recommend documents about previously unrequested content to a specific user. That is, these systems cannot compare and recommend related documents characterized by keywords not previously collected into a given user's profile. Conversely, pure collaborative systems, match only the profiles of users that (to a great extent) have requested exactly the same documents; for instance, different book editions or movie review web sites from different news organizations may be considered distinct documents.

The shortcoming of structural approaches is that they assume that the existing, often static, structure of an information resource contains all the relevant knowledge to be discovered. However, it is often the case that such structure is very poorly designed. On the web in particular, the hypertext links are often not created between important documents, due perhaps to the hurried way in which web sites are created. Indeed, the Web is often more a repository of isolated documents, than a good example of a hypertext fabric. The same applies to the keyword/document relations necessary for LSI.

Collective approaches have the important advantage of adapting to the collective behavior of users, even as it develops in time. This way, a poor initial structure can improve, by creating, strengthening or weakening associations among documents or between documents and keywords. Furthermore, collective recommendation systems can operate without storing individual profiles, thus offering a more private platform for recommendation. Indeed, recommendations are issued according to the adapted structure of the information resources, not according to user profiles. Users can be seen as anonymous social agents. Furthermore, as we shall discuss later, the adapted information resources allow us to capture the knowledge traded by a community of agents. Nonetheless, a disadvantage of collective approaches is that they implement a positive feedback with their communities of users, possibly leading to an excessive adaptation to the interests of a majority of users, thus reducing the diversity of knowledge by recommending only the most retrieved documents in a given area: e.g. the "best of" lists found at Web sites such as - this is the so-called "curse of averages".

It is clear that good recommendation systems require aspects of all approaches to avoid the shortcomings of each individual one. This is the case, for instance, of Fab (Balabanovi and Shoham, 1997) and Amalthaea (Moukas and Maes, 1998), which are both content and collaborative recommendation systems. This way they can discover similar users who have not simply retrieved many of the same exact documents, but documents characterized by many of the same keywords. Furthermore, keywords from documents that users have not actually retrieved, may be added to their profiles because they belong to the profiles of other similar users.

Still, neither Fab nor Amalthaea (nor similar systems) adapt the structure of their information resources with collective user behavior, nor do they use the data-mining techniques of structural algorithms to characterize the knowledge those store. In this sense, they cannot capture the evolving nature of the knowledge of communities of users. In other words, even though they are able to characterize the interests of individual users (both with documents and keywords), the structure of information resources (e.g. Web hyperlink structure or document/keyword matrix) remains unchanged. Furthermore, they rely on individual user profiles, and there is also not an explicit means to discover the knowledge categories that particular communities of users employ. Next I describe the Active Recommendation Project (Rocha and Bollen, 2000) which is building a hybrid Collective/Structural/Content recommendation system designed precisely to tackle these issues. Namely, to adapt information resources to their evolving communities of users, to characterize the knowledge stored in these information resources, and to preserve diversity while not accumulating private user profiles.

2 The Active Recommendation Project

The Active Recomendation Project (ARP), part of the Library Without Walls Project, at the Research Library of the Los Alamos National Laboratory is engaged in research and development of recommendation systems for digital libraries. The information resources available to ARP are large databases with academic articles. These databases contain bibliographic, citation, and sometimes abstract information about academic articles. Typical databases are SciSearch® and Biosis®; the first contains articles from scientific journals from several fields collected by ISI (Institute for Scientific Indexing), while the second contains more biologically oriented publications. We do not manipulate directly the records stored in these information resources, rather, we created a repository of XML (about 3 million) records which point us to documents stored in these databases (Rocha and Bollen (2000).

2.1 Characterizing the Knowledge stored in an Information Resource

We have compiled relational information between records (1) and keywords and among records: the semantics and the structure respectively. The semantics is formalized as a very sparse Keyword-Record Matrix A. The structure is formalized as the very sparse Citation Matrix C, which is a record-record matrix (details in Rocha and Bollen, 2000). From these matrices, we have calculated additional matrices holding measures of closeness between records and between keywords: the Inwards Structural Proximity Matrix or co-citation (Small, 1973), the Outwards Structural Proximity Matrix or bibliographic coupling (Kessler, 1963), the Record Semantic Proximity Matrix (for any two records it is defined by the number of keywords that qualify both, divided by the number of keywords that qualify either one), and the Keyword Semantic Proximity Matrix (for two keywords, it is the number of records they both qualify, over the number of records either one qualifies).

These matrices holding measures of closeness, formally, are proximity relations (Klir an Yuan, 1995; Miyamoto , 1990) because they are reflexive and symmetric fuzzy relations. Their transitive closures are known as similarity relations (Ibid). The collection of this relational information, all the proximity relations as well as A and C, is an expression of the particular knowledge an information resource conveys to its community of users. Notice that distinct information resources typically share a very large set of keywords and records. However, these are organized differently in each resource, leading to different collections of relational information. Indeed, each resource is tailored to a particular community of users, with a distinct history of utilization and deployment of information by its authors and users. For instance, the same keywords will be related differently for distinct resources. Therefore, we refer to the relational information of each information resource as a Knowledge Context. We do not mean to imply that information resources possess cognitive abilities. Rather, we note that the way records are organized in information resources is an expression of the knowledge traded by its community of users. Records and keywords are only tokens of the knowledge that is ultimately expressed in the brains of users. A knowledge context simply mirrors some of the collective knowledge relations and distinctions shared by a community of users.

In (Rocha and Bollen, 2000) we have discussed how these proximity relations are used in ARP. However, the ARP recommendation system described in this article (TalkMine) requires only the Keyword Semantic Proximity (KSP) matrix, obtained from A by the following formula:


The semantic proximity between two keywords, ki and kj, depends on the sets of records indexed by either keyword, and the intersection of these sets. N(ki) is the number of records keyword ki indexes, and N(ki, kj) the number of records both keywords index. This last quantity is the number of elements in the intersection of the sets of records that each keyword indexes. Thus, two keywords are near if they tend to index many of the same records. Table I presents the values of KSP for the 10 most common keywords in the ARP repository.

Table I

From the inverse of KSP we obtain a distance function between keywords:


d is a distance function because it is a nonnegative, symmetric real-valued function such that d(k, k) = 0. It is not an Euclidean metric because it may violate the triangle inequality: d(k1, k2) ≤  d(k1, k3) +  d(k3, k2) for some keyword k3. This means that the shortest distance between two keywords may not be the direct link but rather an indirect pathway. Such measures of distance are referred to as semi-metrics (Galvin and Shore, 1991).

2.2 Characterizing Users

Users interact with information resources by retrieving records. We use their retrieval behavior to adapt the respective knowledge contexts of these resources (stored in the proximity relations). But before discussing this interaction, we need to characterize and define the capabilities of users: our agents. The following capabilities are implemented in enhanced "browsers" distributed to users.

  1. Present interests described by a set of keywords {k1,... , kp}.
  2. History of Information Retrieval (IR). This history is also organized as a knowledge context as described in 2.1, containing pointers to the records the user has previously accessed, the keywords associated with them, as well as the structure of this set of records. This way, we treat users themselves as information resources with their own specific knowledge contexts defined by their own proximity information.
  3. Communication Protocol. Users need a 2-way means to communicate with other information resources in order to retrieve relevant information, and to send signals leading to adaptation in all parties involved in the exchange.

Regarding point 2, the history of IR, notice that the same user may query information resources with very distinct sets of interests. For example, one day a user may search databases as a biologist looking for scientific articles, and the next as a sports fan looking for game scores. Therefore, each enhanced browser allows users to define different "personalities", each one with its distinct history of IR defined by independent knowledge contexts with distinct proximity data (see Figure 1).

Figure 1

Because the user history of IR is stored in personal browsers, information resources do not store user profiles. Furthermore, all the collective behavior algorithms used in ARP do not require the identity of users. When users communicate (3) with information resources, what needs to be exchanged is their present interests or query (1), and the relevant proximity data from their own knowledge context (2). In other words, users make a query, and then share the relevant knowledge they have accumulated about their query, their "world-view" or context, from a particular personality, without trading their identity. Next, the recommendation algorithms integrate the user's knowledge context with those of the queried information resources (possibly other users), resulting in appropriate recommendations. Indeed, the algorithms we use define a communication protocol between knowledge contexts, which can be very large databases, web sites, or other users. Thus, the overall architecture of the recommendation systems we use in ARP is highly distributed between information resources and all the users and their browsing personalities (see Figure 2).

Figure 2

The collective behavior of all users is also aggregated to adapt the knowledge contexts of all intervening information resources and users alike. This open-ended learning process (Rocha, 2000) is enabled by the TalkMine recommendation system described below.

3 Categories and Distributed Memory

3.1 A Model of Categorization from Distributed Artificial Intelligence

TalkMine is both a content-based and collaborative recommendation system based on a model of linguistic categories (Rocha, 1999), which are created from conversation between users and information resources and used to re-combine knowledge as well as adapt it to users. The model of categorization used by TalkMine is described in detail in (Rocha, 1997a, 1999, 2000). Basically, as also suggested by Clark (1993), categories are seen as representations of highly transient, context-dependent knowledge arrangements, and not as model of information storage in the brain. In this sense, in human cognition, categories are seen as linguistic constructs used to store temporary associations built up from the integration of knowledge from several neural sub-networks. The categorization process, driven by language and conversation, serves to bridge together several distributed neural networks, associating tokens of knowledge that would not otherwise be associated in the individual networks. Thus, categorization is the chief mechanism to achieve knowledge recombination in distributed networks leading to the production of new knowledge (Rocha, 1999, 2000).

TalkMine applies such a model of categorization of distributed neural networks driven by language and conversation to DIS and recommendation systems. Instead of neural networks, knowledge is stored in information resources, from which we construct the knowledge contexts with respective proximity relations described in section 2. TalkMine is used as a conversation protocol to categorize the interests of users according to the knowledge stored in information resources, thus producing appropriate recommendations and adaptation signals.

3.2 Distributed Memory is Stored in Knowledge Contexts

A knowledge context of an information resource (section 2.1) is not a connectionist structure in a strong sense since keywords and records are not distributed as they can be identified in specific nodes of the network (van Gelder, 1991). However, the same keyword indexes many records, the same record is indexed by many keywords, and the same record is typically engaged in a citation (or hyperlink) relation with many other records. Losing or adding a few records or keywords does not significantly change the derived semantic and structural proximity relations (section 2) of a large network. In this sense, the knowledge conveyed by such proximity relations is distributed over the entire network of records and keywords in a highly redundant manner, as required of sparse distributed memory models (Kanerva, 1988). Furthermore, Clark (1993) proposed that connectionist memory devices work by producing metrics that relate the knowledge they store. As discussed in section 2, the distance functions obtained from proximity relations are semi-metrics, which follow all of Clark's requirements (Rocha, 2000). Therefore, we can regard a knowledge context effectively as a distributed memory bank. Below we discuss how such distributed knowledge adapts to communities of users (the environment) with Hebbian type learning.

In the TalkMine system we use the KSP relation (formula (1)) from knowledge contexts. It conveys the knowledge stored in an information resource in terms of a measure of proximity among keywords. This proximity relation is unique to each information resource, reflecting the semantic relationships of the records stored in the latter, which in turn echo the knowledge of its community of users and authors. TalkMine is a content-based recommendation system because it uses a keywords proximity relation. Next we describe how it is also collaborative by integrating the behavior of users. A related structural algorithm, also being developed in ARP, is described in (Rocha and Bollen, 2000).

NOTE: The remaining sections are available only in the pdf version.

Due to mathematical formalisms and figures not suitable for HTML


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1. Records contain bibliographical information about published documents. Records can be thought of as unique pointers to documents, thus, for the purposes of this article, the two terms are interchangeable.

For more information contact Luis Rocha at
Last Modified: September 02, 2004