Hybrid Recommender Systems

 

 

 

 

This is a Review for a paper written by Robin Burke, I'll try to summarize it briefly and mention the most important points on this paper.

 

Paper Info.:

Burke, R. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction. 12(4), pages 331-370. [PDF]

 

Paper Abstract:

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
 

Recommender System Definition Past vs. Present

 

Recommender systems were originally defined as “people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients”, like using experts knowledge as input for the system to enrich its ability to recommend to people according to the given knowledge.

The term now has a broader connotation, describing 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.

 

Recommender System vs. Search Engines (Information Retrieval)

 

It is the criteria of “individualized” and “interesting and useful” that separate the recommender system from information retrieval systems or search engines. The semantics of a search engine are “matching”: the system is supposed to return all those items that match the query ranked by degree of match. Unlike recommender system it aims for personalizing the content based on assumptions (Recommender system approaches) which find something similar to user's taste and desire.

 

Recommender System Hybridization

 

All of the known recommendation techniques have strengths and weaknesses, and many researchers have chosen to combine techniques in different ways.

 

Recommendation Techniques

 

 

Comparing Recommendation Techniques 

 

 

Hybrid Recommender Systems Methods

 

 

Possible Recommendation Hybrids

 

The author explains his "EntreeC" experiment by combining Knowledge-based and Collaborative Filtering Approach using Cascade hybrid method and also explained how did he convert his semantic ratings into numeric.

 

The author presented an important information about the Order of Implicit Ratings strength (from Nichols paper,1997).

 

 

 

Hybrid or No Hybrid Choice

 

While the space remains to be fully explored, research has provided some insight into the question of which hybrid to employ in particular situations. The hybridization strategy must be a function of the characteristics of the recommenders being combined.

 

With demographic, content and collaborative recommenders, this is largely a function of the quality and quantity of data available for learning. With knowledge-based recommenders, it is a function of the available knowledge base.

 

We can distinguish two cases: the uniform case, in which one recommender has better accuracy than another over the whole space of recommendation, and the non-uniform case, in which the two recommenders have different strengths in different parts of the space.

 

If the recommenders are uniformly unequal, it may make sense to employ a hybrid in which the inaccuracies of the weaker recommender can be contained: for example, a cascade scheme with the stronger recommender given higher priority, an augmentation hybrid in which the weaker recommender acts as a “bot” contributing a small amount of information, or a meta-level combination in which the stronger technique produces a dense representation that strengthens the performance of the weaker one.

 

In the non-uniform case, the system will need to be able to employ both recommenders at different times. A switching hybrid is a natural choice here, but it requires that the system be able to detect when one recommender should be preferred. Feature combination and mixed hybrids can be used to allow output from both recommenders without having to implement a switching criterion. More research is needed to establish the tradeoffs between these hybridization options.