{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/time-is-of-the-essence-a-joint-hierarchical","title":"Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions","arxiv_id":"1812.01276","date":"2018-12-04","proceeding":null,"authors":["Bjørnar Vassøy","Massimiliano Ruocco","Eliezer de Souza da Silva","Erlend Aune"],"abstract":"In recent years session-based recommendation has emerged as an increasingly\napplicable type of recommendation. As sessions consist of sequences of events,\nthis type of recommendation is a natural fit for Recurrent Neural Networks\n(RNNs). Several additions have been proposed for extending such models in order\nto handle specific problems or data. Two such extensions are 1.) modeling of\ninter-session relations for catching long term dependencies over user sessions,\nand 2.) modeling temporal aspects of user-item interactions. The former allows\nthe session-based recommendation to utilize extended session history and\ninter-session information when providing new recommendations. The latter has\nbeen used to both provide state-of-the-art predictions for when the user will\nreturn to the service and also for improving recommendations. In this work we\ncombine these two extensions in a joint model for the tasks of recommendation\nand return-time prediction. The model consists of a Hierarchical RNN for the\ninter-session and intra-session items recommendation extended with a Point\nProcess model for the time-gaps between the sessions. The experimental results\nindicate that the proposed model improves recommendations significantly on two\ndatasets over a strong baseline, while simultaneously improving return-time\npredictions over a baseline return-time prediction model.","url_abs":"http://arxiv.org/abs/1812.01276v1","url_pdf":"http://arxiv.org/pdf/1812.01276v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"time-is-of-the-essence-a-joint-hierarchical","repo_url":"https://github.com/BjornarVass/Recsys","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"session-based-recommendations","task_name":"Session-Based Recommendations"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.01276","atlas_url":"https://app.syntology.ai/?focus=1812.01276","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01276"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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