CoNet: Co-occurrence neural networks for recommendation
Autor: | Xiuze Zhou, Ming Chen, Yunhao Li |
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Rok vydání: | 2021 |
Předmět: |
Independent and identically distributed random variables
Source code Theoretical computer science Artificial neural network Computer Networks and Communications Computer science media_common.quotation_subject Aggregate (data warehouse) Magic (programming) 020206 networking & telecommunications 02 engineering and technology ENCODE Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Preference (economics) Software media_common |
Zdroj: | Future Generation Computer Systems. 124:308-314 |
ISSN: | 0167-739X |
Popis: | Assuming that both users and items are independent and identically distributed, most existing methods model user–item pairs, while ignoring the relationship between items, leading to limited performance. To solve this problem, we propose a novel neural network, CoNet, which can effectively model the co-occurrence pattern for Collaborative Filtering (CF). We argue that items always occur in pairs, i.e. an item co-occurrence pattern. For example, movies ”Harry Potter 1” and ”Harry Potter 2” are always viewed by users who like magic style films. To learn the latent features, CoNet is simultaneously modeled on user–item and item–item interactions. Compared with methods that train on a single user–item pair, CoNet can encode highly descriptive features from the co-occurrence pattern. To achieve a better performance, we design an attention network to learn the weight of a user’s preference for different items and subsequently aggregate the weighted embeddings to obtain the co-occurrence representations. Finally, we conducted extensive experiments using several data sets, which show that the proposed method is superior to other baseline approaches. Source code of CoNet is available from https://github.com/XiuzeZhou/conet . |
Databáze: | OpenAIRE |
Externí odkaz: |