Matching Recommendation Technologies and Domains - Part 1

 

 

This paper is written by Burke and Ramezani, they introduce a way of mapping between domain characteristics and recommendation technologies, which is very interesting point to talk about.

Paper Info.:
Burke, R. & Ramezani, M. Matching Recommendation Technologies and Domains. In Kantor, P., Ricci, F., Rokach, L. & Shapira, B. eds. Handbook of Recommender Systems. Springer, 2010. (PDF) 

 

Paper Abstract:

Recommender systems form an extremely diverse body of technologies and approaches. The chapter aims to assist researchers and developers identify the recommendation technology that are most likely to be applicable to different domains of recommendation. Unlike other taxonomies of recommender systems, our approach is centered on the question of knowledge: what knowledge does a recommender system need in order to function, and where does that knowledge come from? Different recommendation domains (books vs condominiums, for example) provide different opportunities for the gathering and application of knowledge. These considerations give rise to a mapping between domain characteristics and recommendation technologies.

 

What is a Recommender system?

 

A recommender system is defined by a particular kind of semantics of interaction with the user: “any system that produces individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options”. This expansive definition makes the scope of recommender systems research quite broad, but it fails to give much guidance to the implementer.

 

Related Work (Recommender Systems Taxonomies)

 

The Author introduced 5 papers, which offered different taxonomies.

1. R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User- Adapted Interaction, vol. 12, no. 4, pp. 331–370, 2002.
Contribution: Five recommendation techniques, Advantages and Disadvantages per each, and hybrid methods.
 
2. P. Resnick and H. R. Varian, “Recommender systems,” Commun. ACM, vol. 40, no. 3, pp. 56– 58, 1997. 
Contribution: Identifies 5 dimensions, which characterize properties of users' interactions with the recommender and the aggregation methods of users' evaluations (ratings).
 
3. J. B. Schafer, J. A. Konstan, and J. Riedl, “E-commerce recommendation applications,” Data Mining and Knowledge Discovery, vol. 5, no. 1-2, pp. 115–153, 2001.
Contribution: Taxonomy of collaboratice e-commerce recommender applications into 3 categories: functional I/O, the recommendation method, and other design issues.
 
4. M. Montaner, B. L´opez, and J. L. D. L. Rosa, “A taxonomy of recommender agents on the Internet,” Artif. Intell. Rev., vol. 19, no. 4, pp. 285–330, 2003.
Contribution: using two main criteria: user profile generation and maintenance, and user profile exploitation (Common patterns are extracted by an analysis of systems in the same domain).
 
5. G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.
Contribution: Comparing between 3 main categories (content, collaborative, and hybrid.. omitting knowledge-based and classifies recommenders in each category into either heuristics-based or model-based).

Paper Contribution

AI-centric approach, focused on the knowledge sources required for recommendation and the constraints related to them. The chapter discusses the applicability of different recommendation techniques to different types of problems and aims to guide decision making in choosing among these techniques. As such, it might be considered to serve as a sort of recommender for recommender system implementers.

Knowledge Sources

 

• Social: Knowledge about the larger community of users other than the target user.
• Individual: Knowledge about the target user.
• Content: Knowledge about the items being recommended and, more generally, about their uses.

The Recommender System use the knowledge sources based on the type of problem it aimed to solve.

Recommendation types vs Knowledge Sources

 

• Collaborative recommendation matches an individual knowledge source with a social knowledge source of the same type and extrapolates the target user’s preferences from his or her peers. Usually, in collaborative recommendation, individual requirements are not used, or applied very simply as filters.


• Content-based recommendation on the other hand is individually-focused, using item features and user opinions to learn a classifier that can predict user preferences on new items.


• Knowledge-based recommendation is more of a catch-all category in which the recommender applies any kind of domain knowledge more substantive than item features.

Domain Characteristics

A domain of recommendation is the set of items that the recommender will operate over, but may also include the set of aims or purposes that the recommender is intended to support. These characteristics of the domain affect the availability and utility of different knowledge sources.

Six important characteristics of the domain that an implementer should consider:

1. Heterogeneity: A heterogeneous item space encompasses many items with different characteristics and most importantly, different goals they can satisfy.

2. Risk: Recommendation domains can be distinguished by the degree of risk that a user incurs in accepting a recommendation.

3. Churn: Recommender systems are used in domains with long-lived items like books, but they are also used in domains where the value or relevance of an item has a very short time span, such as news stories. A high churn domain is one in which items come and go rapidly.

4. Interaction Style: In systems in which the user makes no special effort to interact with the recommender system, the system extracts the implicit expressed preferences from user behaviour.

5. Preference Stability: User preferences can also have varying degrees of duration.

6. Scrutability: Certain applications (for example, high-risk ones) may require that the system be able to explain its recommendations, to answer questions like “why was this item Matching Recommendation Technologies and Domains recommended?” Such explanations enhance user confidence that a recommendation is appropriate and increase the liklihood of recommendations being accepted.