Recurrent Neural Networks (RNN) is a frequently used technique for sequence data predictions. Recently, it gains popularity in the Recommender Systems domain, especially for session-based recommendations where naturally, each session is defined as a sequence of clicks, and timestamped data per click is available. In our research, in its early stages, we explore the value of incorporating dwell time into existing RNN framework for session-based recommendations by boosting items above the predefined dwell time threshold. We show improvement in recall@20 and MRR@20 by evaluating the proposed approach on e-commerce RecSys’15 challenge dataset.
|Number of pages||3|
|Journal||CEUR Workshop Proceedings|
|State||Published - 2017|
|Event||1st Workshop on Temporal Reasoning in Recommender Systems, RecTemp 2017 - Como, Italy|
Duration: 27 Aug 2017 → 31 Aug 2017
Bibliographical noteFunding Information:
The work is partially supported by the Israeli Innovation Authority, the Ministry of Economy and Industry, MAGNET “Infomedia” project.
Copyright © 2017 for this paper by its authors.
- Deep learning
- Dwell time
- Recommender systems
- Recurrent neural networks
- Temporal aspects
ASJC Scopus subject areas
- Computer Science (all)