PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset

Choudhary, Priyanka and Desarkar, Maunendra Sankar (2018) PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset. In: ECommerce Workshop in SIGIR 2018, 8th-12th July 2018, Michigan, USA.

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Recommendation systems create personalized list of items that might interest the user by analyzing the user’s history of past purchases and/or consumption. For rating based systems, most of the traditional methods for recommendation focus on the absolute ratings provided by the users to the items. In this paper, we extend the traditional Matrix Factorization approach for recommendation and propose pairwise relation based factor modeling. While modeling the items in the system, the use of pairwise preferences allow information flow between the items through the preference relations as an additional information. Item feedbacks are available in the form of reviews apart from the rating information. The reviews have textual information that can be really helpful to represent the item’s latent feature vector appropriately. We perform topic modeling of the item reviews and use the topic vectors to guide the joint factor modeling of the users and items and learn their final representations. The proposed method shows promising results in comparison to the state-of-the-art methods in our experiments.

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IITH Creators:
IITH CreatorsORCiD
Desarkar, Maunendra SankarUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Simplex, ASC, EMD
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 26 Jun 2018 04:47
Last Modified: 26 Jun 2018 04:48
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