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Session Recommendation via Recurrent Neural Networks over Fisher Embedding Vectors

1
Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), H-1111 Budapest, Hungary
2
Faculty of Informatics, Eötvös University, Pázmány sétány 1/C, H-1117 Budapest, Hungary
3
Széchenyi University, Egyetem tér 1, H-9026 Győr, Hungary
*
Author to whom correspondence should be addressed.
This paper is the extended version of Daróczy, B.; Ayala-Gómez, F.; Benczúr, A. Infrequent Item-to-Item Recommendation via Invariant Random Fields. In Proceedings of the Mexican International Conference on Artificial Intelligence, Guadalajara, Mexico, 22–27 October 2018; Springer: Cham, Switzerland, 2018; pp. 257–275.
Sensors 2019, 19(16), 3498; https://doi.org/10.3390/s19163498
Received: 23 May 2019 / Revised: 14 July 2019 / Accepted: 4 August 2019 / Published: 10 August 2019
(This article belongs to the Special Issue Artificial Intelligence and Sensors)
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Abstract

Recommendation services bear great importance in e-commerce, shopping, tourism, and social media, as they aid the user in navigating through the items that are most relevant to their needs. In order to build recommender systems, organizations log the item consumption in their user sessions by using different sensors. For instance, Web sites use Web data loggers, museums and shopping centers rely on user in-door positioning systems to register user movement, and Location-Based Social Networks use Global Positioning System for out-door user tracking. Most organizations do not have a detailed history of previous activities or purchases by the user. Hence, in most cases recommenders propose items that are similar to the most recent ones viewed in the current user session. The corresponding task is called session based, and when only the last item is considered, it is referred to as item-to-item recommendation. A natural way of building next-item recommendations relies on item-to-item similarities and item-to-item transitions in the form of “people who viewed this, also viewed” lists. Such methods, however, depend on local information for the given item pairs, which can result in unstable results for items with short transaction history, especially in connection with the cold-start items that recently appeared and had no time yet to accumulate a sufficient number of transactions. In this paper, we give new algorithms by defining a global probabilistic similarity model of all the items based on Random Fields. We give a generative model for the item interactions based on arbitrary distance measures over the items, including explicit, implicit ratings and external metadata to estimate and predict item-to-item transition probabilities. We exploit our new model in two different item similarity algorithms, as well as a feature representation in a recurrent neural network based recommender. Our experiments on various publicly available data sets show that our new model outperforms simple similarity baseline methods and combines well with recent item-to-item and deep learning recommenders under several different performance metrics. View Full-Text
Keywords: recommender systems; recurrent neural networks; fisher information; markov random fields recommender systems; recurrent neural networks; fisher information; markov random fields
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kelen, D.; Daróczy, B.; Ayala-Gómez, F.; Ország, A.; Benczúr, A. Session Recommendation via Recurrent Neural Networks over Fisher Embedding Vectors. Sensors 2019, 19, 3498.

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