@inproceedings{21f33b289f834ff0899c8e9e49fc9e1c,
title = "Perceiving the next choice with comprehensive transaction embeddings for online recommendation",
abstract = "To predict customer{\textquoteright}s next choice in the context of what he/she has bought in a session is interesting and critical in the transaction domain especially for online shopping. Precise prediction leads to high quality recommendations and thus high benefit. Such kind of recommendation is usually formalized as transaction-based recommender systems (TBRS). Existing TBRS either tend to recommend popular items while ignore infrequent and newly-released ones (e.g., pattern-based RS) or assume a rigid order between items within a transaction (e.g., Markov Chain-based RS) which does not satisfy real-world cases in most time. In this paper, we propose a neural network-based comprehensive transaction embedding model (NTEM) which can effectively perceive the next choice in a transaction context. Specifically, we learn these comprehensive embeddings of both items and their features from relaxed ordered transactions. The relevance between items revealed by the transactions is encoded into such embeddings. With rich information embedded, such embeddings are powerful to predict the next choices given those already bought items. NTEM is a shallow wide-in-wide-out network, which is more efficient than deep networks considering large numbers of items and transactions. Experimental results on real-world datasets show that NTEM outperforms three typical TBRS models FPMC, PRME and GRU4Rec in terms of recommendation accuracy and novelty. Our implementation is available at https://github.com/shoujin88/NTEM-model.",
author = "Shoujin Wang and Liang Hu and Longbing Cao",
year = "2017",
doi = "10.1007/978-3-319-71246-8_18",
language = "English",
isbn = "9783319712451",
series = "Lecture Notes in Artificial Intelligence",
publisher = "Springer, Springer Nature",
pages = "285--302",
editor = "Michelangelo Ceci and Jaakko Hollm{\'e}n and Ljup{\v c}o Todorovski and Celine Vens and Sa{\v s}o D{\v z}eroski",
booktitle = "Machine Learning and Knowledge Discovery in Databases",
address = "United States",
note = "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2017 ; Conference date: 18-09-2017 Through 22-09-2017",
}