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. Masters thesis, Indian Institute of Technology Hyderabad.

Thesis_Mtech_CS_4074.pdf - Published Version

Download (853kB) | Preview


Recommendation systems create personalized list of items that might interest the user by analyzing the user’s history of past purchases and/or consumption. Generally only a small subset of the items are assessed by each user, and from the large subset of unseen items, the systems need to produce an accurate list of recommendations. 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 work, we extend the traditional Matrix Factorization approach for recommendation and propose pairwise relation based factor modeling. We propose the method based on the pairwise preferences between the items to capture the relative tendency of user selecting one item over the other. 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 methods shows promising results in comparison to the state-of-the-art methods in our experiments. vi

[error in script]
IITH Creators:
IITH CreatorsORCiD
Desarkar, Maunendra SankarUNSPECIFIED
Item Type: Thesis (Masters)
Uncontrolled Keywords: Recommendation System, Pairwise Preferences, Topic Modeling
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 26 Jun 2018 04:39
Last Modified: 26 Jun 2018 04:39
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 4074 Statistics for this ePrint Item