TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures |
| 영문제목(English Title) |
Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures |
| 저자(Author) |
Kala K. U.
M. Nandhini
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| 원문수록처(Citation) |
VOL 14 NO. 02 PP. 0538 ~ 0561 (2020. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, moviewatching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.
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| 키워드(Keyword) |
Context Aware Recommender System
Recurrent Neural Network
Sequence Aware Recommender System
Dynamic Temporal Matching
Deep Recommender System Models.
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| 파일첨부 |
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