Browse State-of-the-Art › Movie Recommendation
Movie Recommendation
29 papers with code · 1 benchmark · 2 datasets archive 2025-07-28
Evaluates the ability of language models to propose relevant movie recommendations with collaborative filtering data.
Source: BIG-bench
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| MovieLens 1M (5 rows) | BPR | Post Processing Recommender Systems with Knowledge Graphs for... | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
2 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
29 shown of 29 papers with code (113 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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8 Dec 2021 3 repositories listedLanguage modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
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14 Jul 2018 3 repositories listedOur approach combines movie embeddings (learned from a sibling VAE network) with user ratings from the Movielens 20M dataset and applies it to the task of movie recommendation.
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25 Oct 2023 2 repositories listedRecently, large language models (LLMs) have exhibited significant progress in language understanding and generation.
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30 Mar 2023 2 repositories listedThe use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering.
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29 Mar 2022 2 repositories listed Syntology ran 8 of 11 samples · 3 unverified · 4 pointer-only (licence)We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
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26 Aug 2016 2 repositories listedWe show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach.
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24 Sep 2024 1 repository listedIn specific domains like fashion, music, and movie recommendation, the multi-faceted features characterizing products and services may influence each customer on online selling platforms differently, paving the way to…
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14 Jun 2024 1 repository listedWe consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content.
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22 May 2024 1 repository listedIn this work, we present a novel algorithm for submodular maximization subject to a cardinality constraint that combines a guarantee of $0.
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31 Oct 2023 1 repository listedThe recommendation model known as LFM (Latent Factor Model), which captures latent features through matrix factorization and gradient descent to fit user preferences, has given rise to various recommendation algorithms…
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16 Oct 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverifiedRecommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development.
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29 Jun 2023 1 repository listedWe introduce ordered transfer hyperparameter optimisation (OTHPO), a version of transfer learning for hyperparameter optimisation (HPO) where the tasks follow a sequential order.
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24 May 2023 1 repository listedStreaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset.
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5 Oct 2022 1 repository listedAs virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dialogue tasks such as hotel booking,…
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4 Sep 2022 1 repository listedIn this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation tasks by extracting knowledge entities from movie reviews and capturing understandable interactions…
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22 Aug 2022 1 repository listedIn the proposed framework, we use an attention-based multi-hop propagation mechanism to take users and movies as center nodes and extend their attributes along with the connections of the knowledge graph by recursively…
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7 Jul 2022 1 repository listedIn the context of online interactive machine learning with combinatorial objectives, we extend purely submodular prior work to more general non-submodular objectives.
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24 Apr 2022 1 repository listedExisting explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.
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6 Nov 2021 1 repository listedWhile most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches.
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1 Jun 2021 1 repository listedWe call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user{'}s sentiment towards an entity from the conversation context, and 2) transforms the ratings of…
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8 May 2021 1 repository listedThe purpose of the task is to increase the evaluation power of user simulations and to make the simulation more human-like.
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3 May 2021 1 repository listedPublic knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems.
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14 Oct 2020 1 repository listedSubmodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.
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29 Sep 2020 1 repository listedTo better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs.
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28 Aug 2020 1 repository listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses.
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9 Feb 2020 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization.
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18 Sep 2019 1 repository listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)We consider the problem of learning to play a repeated multi-agent game with an unknown reward function.
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25 Feb 2019 1 repository listedThe present project is inspired by the LIDA model to apply it to the process of movie recommendation, the model called MIRA (Movie Intelligent Recommender Agent) presented percentages of precision similar to a…
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27 Nov 2018 1 repository listedTraditional approaches in recommendation systems include collaborative filtering and content-based filtering.
Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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