Papers by Nigoraxon G'aniyeva
SN Computer Science, 2020
Recommending relevant items of interest for a user is the main purpose of the recommendation syst... more Recommending relevant items of interest for a user is the main purpose of the recommendation system. In the past, those systems achieve the recommended list based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, the recommendation system needs to reduce the dependence on user profiles while preserving high accuracy on the recommendation. Session-based recommendation is a recently proposed approach for the recommendation system to overcome the issue of user profiles dependency. The relevance of the problem is quite high and has triggered interest among researchers in observing the activities of users. It increased several proposals for session-based recommendation algorithms that aim to predict the next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. A deep learning approach named GRU4REC (Hidasi et al. in Session-based recommendations with recurrent neural networks, 2015) and simpler methods are included in our comparison. Real-world datasets from three different domains are included in our experiment. Our experiments reveal that in some cases of numerous unpopular items dataset, GRU4REC's performance is lower than expected. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol ... more Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
The problem of session-based recommendation aims to predict user actions based on anonymous sessi... more The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.
Recommending relevant items of interest for a user is the main purpose of the recommendation syst... more Recommending relevant items of interest for a user is the main purpose of the recommendation system. In the past, those systems achieve the recommended list based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, the recommendation system needs to reduce the dependence on user profiles while preserving high accuracy on the recommendation. Session-based recommendation is a recently proposed approach for the recommendation system to overcome the issue of user profiles dependency. The relevance of the problem is quite high and has triggered interest among researchers in observing the activities of users. It increased several proposals for session-based recommendation algorithms that aim to predict the next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. A deep learning approach named GRU4REC (Hidasi et al. in Session-based recommendations with recurrent neural networks, 2015) and simpler methods are included in our comparison. Real-world datasets from three different domains are included in our experiment. Our experiments reveal that in some cases of numerous unpopular items dataset, GRU4REC's performance is lower than expected. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.
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Papers by Nigoraxon G'aniyeva