Experimenting Analogical Reasoning in Recommendation

Abstract

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract. Recommender systems aim at providing suggestions of interest for end-users. Two main types of approach underlie existing recommender systems: content-based methods and collaborative filtering. In this paper, encouraged by good results obtained in classification by analogical proportion-based techniques, we investigate the possibility of using analogy as the main underlying principle for implementing a prediction algorithm of the collaborative filtering type. The quality of a recommender system can be estimated along diverse dimensions. The accuracy to predict user's rating for unseen items is clearly an important matter. Still other dimensions like coverage and surprise are also of great interest. In this paper, we describe our implementation and we compare the proposed approach with well-known recommender systems.

Analogy in recommendation. Numerical vs. ordinal: A discussion

“…The idea of applying analogy to recommendation is not entirely new. Thus, Sakaguchi et al [8] use four-terms analogy in a case-based reasoning style for proposing dishes to users, while three of the authors of the present paper have more recently proposed a 4-(situation, conclusion)-based analogical mechanism for predicting missing ratings on the basis of known ratings [9]. This latter work yielded reasonably good results, but was extremely heavy computationally speaking.…”

“…Using analogical reasoning for recommendation as been studied in [9]. Authors strictly follow the analogical inference principle described in section II-B for making predictions, using analogical proportions between users.…”

“…3) setr ui as an aggregate of all candidate ratings. This technique has shown to be not too far from basic collaborative filtering approaches [9], but suffers of its inherent cubic complexity which makes it impossible to look for every possible 3-tuples of users, thus compromising the prediction accuracy.…”

Using an analogical reasoning framework to infer language patterns for negative life events

“…Analogical reasoning has recently attracted increased attention in the field of artificial intelligence because an analogy is a very basic form of logical inference [9]. It has been widely applied to natural language processing (NLP) tasks such as question answering [10–12], word segmentation [13], latent relational analysis [14, 15], and recommendation systems [16].…”