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Sequence-Aware Recommender Systems
[article]
<span title="2018-02-23">2018</span>
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arXiv
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we call sequence-aware recommender systems, and outline open challenges in the area. ...
Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what ...
Other approaches rely on a cascade of algorithms [40] , where a sequential model is used to filter the predictions generated by a sequence-agnostic one, or a meta-level approach [87] , where a first ...
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Sequence-Aware Recommender Systems
<span title="2018-07-06">2018</span>
<i title="Association for Computing Machinery (ACM)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eiea26iqqjcatatlgxdpzt637y" style="color: black;">ACM Computing Surveys</a>
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we call sequence-aware recommender systems, and outline open challenges in the area. ...
Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what ...
Other approaches rely on a cascade of algorithms [40] , where a sequential model is used to filter the predictions generated by a sequence-agnostic one, or a meta-level approach [87] , where a first ...
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Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks
[chapter]
<span title="2021-08-31">2021</span>
<i title="IOS Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qv2vo27d4zghvkz2dhzcboly5e" style="color: black;">Applications and Practices in Ontology Design, Extraction, and Reasoning</a>
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In this paper, we investigate how well state-of-the-art approaches do exploit those different dimensions relevant to POI recommendation tasks. ...
Learned latent vector representations are key to the success of many recommender systems in recent years. ...
in a POI recommendation task. ...
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Recommendation Through Mixtures of Heterogeneous Item Relationships
[article]
<span title="2018-08-29">2018</span>
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arXiv
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Our method borrows ideas from mixtures-of-experts approaches as well as knowledge graph embeddings. ...
Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data. ...
In addition, CNN-based approaches have also shown competitive performance on sequential and session-based recommendation [36, 38] . 'Relationship-aware' Recommendation. ...
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A Composite of Heterogeneous Sources Recommenders (CHR)
<span title="2020-03-17">2020</span>
<i title="Foundation of Computer Science">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a>
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We see that our approach produces more specific recommendations than other options and also presenting instinctive explanations behind the recommendations. ...
Our method adopts ideas from knowledge graph representations as well as several expert networks where each of them specializes in a different part of input space. ...
Finally, the goal of sequential recommendation is to rank the next-item higher than unrelated items; the loss function we use is defined as: (7) Where We utilize a multi-task learning method mutually ...
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A Survey on Session-based Recommender Systems
[article]
<span title="2021-05-15">2021</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. ...
We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. ...
Several survey papers focus particularly on SRSs, including sequence-aware recommender systems [91] , deep learning for sequential recommendations [27] and sequential recommender systems [128] . ...
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A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations
[article]
<span title="2020-11-20">2020</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. ...
A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable ...
[109] propose a novel Hyperbolic Metric Embedding (HME) approach for the next-POI recommendation task. ...
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Graph Neural Networks in Recommender Systems: A Survey
[article]
<span title="2022-04-02">2022</span>
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arXiv
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in graph representation learning. ...
Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. ...
Gowalla is a classical dataset for POI recommendation and adopted in user-item collaborative filtering and sequential recommendation as well. ...
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Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
[article]
<span title="2022-01-19">2022</span>
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arXiv
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Specifically, we formulate the in-stream geo-human interaction modeling problem into a novel deep interactive reinforcement learning framework, where an agent is a recommender and an action is a next POI ...
We uniquely model the reinforcement learning environment as a joint and connected composition of users and geospatial contexts (POIs, POI categories, functional zones). ...
To overcome the sparsity challenge, we propose and leverage predefined meta-paths to select the most relevant POI candidates for recommendation with awareness of semantics and interests' context. ...
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Research Commentary on Recommendations with Side Information: A Survey and Research Directions
[article]
<span title="2019-11-08">2019</span>
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arXiv
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One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models. ...
To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree ...
With knowledge graphs as an example, the current deep learning based recommendation approaches are limited to the usage of the most basic information in a knowledge graph, for example, paths or meta-paths ...
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A Survey on Reinforcement Learning for Recommender Systems
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<span title="2022-03-24">2022</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2021-09-22">2021</span>
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recommendatin, sequential recommendation, and explainable recommendation. ...
To this end, we firstly provide a thorough overview, comparisons, and summarization of RL approaches applied in four typical recommendation scenarios, including interactive recommendation, conversational ...
Fortunately, meta-RL, such as Meta-Strategy [136] and Meta-Q-Learning [137] , first learns a large number of RL tasks to obtain enough prior knowledge, and later can be quickly adapted to the new environment ...
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Graph Neural Networks in Recommender Systems: A Survey
<span title="2022-05-05">2022</span>
<i title="Association for Computing Machinery (ACM)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eiea26iqqjcatatlgxdpzt637y" style="color: black;">ACM Computing Surveys</a>
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in graph representation learning. ...
Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. ...
Gowalla is a classical dataset for POI recommendation and adopted in user-item collaborative iltering and sequential recommendation as well. ...
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A Survey on Knowledge Graph-Based Recommender Systems
[article]
<span title="2020-02-28">2020</span>
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arXiv
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Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. ...
In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. ...
Next, it summarizes explainable rules, which is in the form of meta-paths in the KG. The corresponding weight for each rule is further learned. ...
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GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network
<span title="2020-07-29">2020</span>
<i title="Springer Science and Business Media LLC">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4pfqfq76vvcxljfl7mvdl37n5q" style="color: black;">Data Science and Engineering</a>
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the collaborative, sequential and content-aware information. ...
Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users' attributes or knowing the detailed features of the vast number ...
NEXT [29] incorporates meta-data information, time interval and visit time, and leverages the DeepWalk method to encode such knowledge. ...
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Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
[article]
<span title="2020-10-10">2020</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2019-11-26">2019</span>
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In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ...
Specifically,we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting ...
[25] proposed 2 (Scenario-specific Sequential Meta learner) which combines the scenario-specific learning with a model-agnostic sequential meta learning for more effective recommendation. ...
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