Recommendation system with multi-dimensional and parallel-case four-term analogy

2013

Log. Univers.

Given a 4-tuple of Boolean variables (a, b, c, d), logical proportions are modeled by a pair of equivalences relating similarity indicators (a ∧ b and a ∧ b), or dissimilarity indicators (a ∧ b and a ∧ b) pertaining to the pair (a, b), to the ones associated with the pair (c, d). There are 120 semantically distinct logical proportions. One of them models the analogical proportion which corresponds to a statement of the form "a is to b as c is to d". The paper inventories the whole set of logical proportions by dividing it into 5 subfamilies according to what they express, and then identifies the proportions that satisfy noticeable properties such as full identity (the pair of equivalences defining the proportion hold as true for the 4-tuple (a, a, a, a)), symmetry (if the proportion holds for (a, b, c, d), it also holds for (c, d, a, b)), or code independency (if the proportion holds for (a, b, c, d), it also holds for their negations (a, b, c, d)). It appears that only 4 proportions (including analogical proportion) are homogeneous in the sense that they use only one type of indicator (either similarity or dissimilarity) in their definition. Due to their specific patterns, they have a particular cognitive appeal, and as such are studied in greater details. Finally, the paper provides a discussion of the other existing works on analogical proportions.

Section: Other Computational Approachesmentioning

confidence: 99%

From Analogical Proportion to Logical Proportions

2013

Log. Univers.

“…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.…”

Section: Introductionmentioning

confidence: 71%

Analogy in recommendation. Numerical vs. ordinal: A discussion

Hug

2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

et al. 2016

The paper investigates the use of analogical reasoning for recommendation purposes. More particularly, we address the problem of predicting missing ratings on the basis of known ones. After discussing the differences with another recently experimented approach based on analogical proportions, a new analogical approach is proposed. It relies on the intuition that "the rating of user u for item i is to the rating of user v for item i as the rating of user u for item j is to the rating of user v for item j". This leads to algorithms yielding results close to the ones of state-of-the art approaches, when the ratings are regarded as numerical quantities. This is due to the fact that these latter approaches embed an estimation process that is implicitly close to analogy, as discussed in this paper. An analogical approach is also outlined and briefly discussed when the ratings are supposed to have an ordinal meaning only.

“…Lastly, the ideas of the exploitation of the creative power of analogy for (i) proposing items never considered by a user, but having some noticeable common features with items (s)he likes [15], (ii) of the explanation power of analogy for suggesting to the user why an item may be of interest for him, are still entirely to explore.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Experimenting Analogical Reasoning in Recommendation

Hug

Lecture Notes in Computer Science

2015

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.