RecommenderSystems

2024 Learning Hierarchical Preferences for Recommendation with Mixture Intention Neural Stochastic Processes 🔗 https://ieeexplore.ieee.org/abstract/document/10378947 Abstract: User preferences behind users' decision-making processes are highly diverse and may range from lower-level concepts with more specific intentions and higher-level concepts with more general intentions. In this case, user preferences tend to be expressed hierarchically. However, learning such intentions with different levels from user behaviors is challenging, and remains largely neglected by the existing literature. Meanwhile, user behavior data tends to be sparse because of the limited user response and the vast combinations of users and items, which results in cold-start problems with unclear user intentions. In this paper, we propose a mixture intention neural stochastic process (MINSP), a new view of the stochastic processes family using a general meta-learning mechanism and mixture strategy for robust recommendation with hierarchical preferences modeling. By considering the recommendation process for each user as a stochastic process, MINSP defines distributions over functions and is capable of rapid adaptation to different users. To capture the user's intention on different levels, an iterative additive algorithm is proposed that minimizes the approximation error by back fitting the residuals of previous approximations. In this case, the induced tree intention hierarchies serve as an aggregated structured representation of the whole preference, summarizing the gist for convenient navigation and better generalization. Furthermore, we theoretically analyze the generalization error bound of the proposed MINSP to guarantee the model performance. Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines in terms of recommendation performance, and obtain an interpretable hierarchical intention structure. #Stochastic #Processes #StochasticProcesses #Stochastic_Processes #Task #Analysis #TaskAnalysis #Task_Analysis #Predictive #Models #PredictiveModels #Predictive_Models #Metalearning #Behavioral #Sciences #BehavioralSciences #Behavioral_Sciences #Adaptation #AdaptationModels #Adaptation_Models #Uncertainty #Hierarchical #Preferences #HierarchicalPreferences #Hierarchical_Preferences #UserPreferenceModeling #User_Preference_Modeling #User #Preference #Modeling #Process #StochasticProcess #Stochastic_Process #Neural #MINSP @Recommender_Systems

🔅 #نقشه_راه برای آنهایی که تمایل به یادگیری شخصی و خود خوان هستند، مفید است. سایت Roadmaps نقشه راه مناسب برای هر توسعه دهنده را بصورت قدم به قدم ارایه می دهد به تازگی دو نقشه AI and Data Scientist Roadmap و Technical Writer به سایت اضافه شده است ▫️Roadmap.sh is the 6th most starred project on GitHub and is visited by hundreds of thousands of developers every month. It is a community effort to create roadmaps, guides and other educational content to help guide developers in picking up a path and guide their learnings. ❗️دیگر مطالب مرتبط ▫️مسیر دانشمند داده ▫️نقشه کامل راه یادگیری علم داده در سال 2024 ▫️نقشه راه فناوری های نوظهور ▫️Data Science RoadMap ▫️Python Roadmap ▫️Use this Roadmap to Learn Python ▫️Python Tutorials and Roadmap ▫️The Real Python Developer Road-map #Roadmap #Roadmaps #Community #Guides #Developer #Path #Skill #Best #Practices #BestPractices #Best_Practices #GitHub #Learn #Comprehensive #Code #Videos #Video #Guide @Recommender_Systems

✅ یک منبع عالی شامل گلچینی از بهترین مقالات در زمینه ریکامندرها ❗️ Must-read papers on Recommender System 🌐 https://github.com/hongleizhang/RSPapers 🔅 This repository provides a curated list of papers and tutorials about Recommender Systems (RS) including systematic tutorials, comprehensive surveys, general recommender system, social recommender system, deep learing-based recommender system, cold start problem in recommender system, efficient recommender system, exploration and exploitation problem in recommender system, explainability in recommender system as well as click through rate prediction for recommender system, knowledge graph for recommeder system, review based recommender system, conversational recommender system, industrial/practical recommender system and privacy preserving recommender system. #Repository #Curated #List #Papers #Tutorials #RS #Systematic #Comprehensive #Surveys #General #Social #Exploration #Exploitation #Explainability #Rate #Prediction #Knowledge #Graph #Review #Conversational #Industrial #Practical #Privacy #Preserving #GitHub @Recommender_Systems

2024 An empirical study on metamorphic testing for recommender systems 🔗 https://www.sciencedirect.com/science/article/abs/pii/S0950584924000156 Abstract Context: Recommender systems are widely used in various fields because they can provide decision-making guidance to users facing an overwhelming set of choices. In previous studies, the accuracy of recommendations has been the focus and has significantly improved. However, the quality issues of these systems have been overlooked. In practical applications, the reliability of recommender systems plays an important role in their acceptance by users. Objective: This paper aims to develop a solution for performing metamorphic testing on recommender systems, and then to evaluate their reliability based on the test results. Methods: A metamorphic testing framework for recommender systems is first proposed to effectively alleviate the difficulty of the test oracle (i.e., the construction of the expected output of a program). Meanwhile, a set of specific metamorphic relations for recommender systems is also designed, and an empirical analysis is conducted using three open-source recommender libraries: LibRec, PREA, and Surprise. Results: The effectiveness of the proposed metamorphic testing solution is confirmed through the experiments, and the comparison analysis of the designed metamorphic relations and the three recommender libraries is also conducted, yielding the rankings of both the metamorphic relations and the program libraries, respectively. Conclusion: The study suggests that metamorphic testing is effective in automatically revealing the reliability problems in recommender systems, without requiring test oracles. #Empirical #Study #Metamorphic #Testing #Test #Reliability @Recommender_Systems

2024 A Survey on Data-Centric Recommender Systems 🔗 https://arxiv.org/pdf/2401.17878.pdf Abstract: Recommender systems (RS) have become essential tools for mitigating information overload in a range of real-world scenarios. Recent trends in RS have seen a paradigm shift, moving the spotlight from model-centric innovations to the importance of data quality and quantity. This evolution has given rise to the concept of data-centric recommender systems (Data-Centric RS), marking a significant development in the field. This survey provides the first systematic overview of Data-Centric RS, covering 1) the foundational concepts of recommendation data and Data-Centric RS; 2) three primary issues in recommendation data; 3) recent research developed to address these issues; and 4) several potential future directions in Data-Centric RS. #Survey #Data #Centric #DataCentric #Data_Centric #Arxiv #Systematic #Overview @Recommender_Systems

2024 Distinguishing latent interaction types from implicit feedbacks for recommendation Abstract: For privacy considerations, many recommendation algorithms are only based on a kind of implicit feedbacks, where various user behaviors, like view, purchase and etc., are simplified as binary interactions. As side information is assumed not available, these algorithms mainly focus on how to encode users and items via a bipartite graph (viz. the user-item interaction matrix), while ignoring to encode edges for distinguishing the interaction types. In this paper, we argue that implicit feedbacks can be classified into a few user-item relations (viz., latent interaction types) via encoding the edges of a bipartite graph. In particular, we design an edge distinguishment module (EDM) into our neural recommendation model, called Relation-Aware Neural Model (RANM). Based on the latent interaction types, we divide the bipartite graph into a few subgraphs, each consisting of only edges of the same relation and their connected user and item nodes. We propose a Relation-Aware Graph Neural Network (RAGNN) for learning user and item representations. For encoding items, we apply the RAGNN on the relation-aware bipartite graph; While for encoding a user, we first encode several latent interests each on one subgraph and then fuse these interest encodings as the user representation. Experiments on three public datasets validate that our approach of edge classification and representation learning help improving recommendation performance compared with the state-of-the-art competitors. The implementations are available at https://github .com /lulu0913 /RAGNN #Distinguishing #Latent #Interaction #Implicit #Feedback #ImplicitFeedback #Implicit_Feedback #Behavior #EDM #RANM #RAGNN #Implementation #Graph #Neural #RAGNN #GNN #Relation #State_of_the_Art #StateoftheArt #Edge #RelationAware #Relation_Aware #Latent @Recommender_Systems

2024 SSE4Rec Sequential recommendation with subsequence extraction Abstract Sequential recommendation mines the sequential patterns in user behavior data to recommend items to users. Recent studies have mainly followed the language modeling paradigm, on the premise that the next item depends on the sequence of previous items. Notably, differences exist between user behavior and textual data. One key difference is that behavioral sequences can encompass multiple intentions, unlike sentences that typically express a single intention. Furthermore, behavioral sequences emerge freely from users, whereas sentences conform to grammatical rules. This study highlights the risk of treating behavior sequences as a unified sequence, and the resultant potential for overfitting the observed transitions. We mitigated this risk by using subsequence extraction for recommendation (SSE4Rec). This model employs a subsequence extraction module that disperses items into distinct subsequences and groups of related items. Each subsequence is then processed by an independent downstream sequence model, which discourages the memorization of inconsequential transitions. Both the training and inference strategies are inherently integrated into the model. The proposed method was evaluated on four public datasets, whereby it was demonstrated to outperform publicly available alternatives or deliver competitive results. The properties of the model were also explored, further visualizing the output of the subsequence extraction module. #SSE4Rec #Sequential #SubsequenceExtraction #Subsequence_Extraction #Subsequence #Extraction #Implicit #Feedback #ImplicitFeedback #Implicit_Feedback #Behavior @Recommender_Systems

2024 MGT: Multi-Granularity Transformer leveraging multi-level relation for sequential recommendation Abstract Sequential recommendation has been a popular research topic in recent times, aiming to predict the next item in a user’s sequence based on their past behaviors. Self-Attention (SA)-based models have shown state-of-the-art performance in this domain. These SA-based models adopt vanilla self-attention mechanism, which takes every single item as the minimum modeling unit and is sufficient to capture the point-level relation: several previously interacted items affecting the target item individually. However, we argue that vanilla self-attention mechanism in existing SA-based models neglects the collective influence of a group of items and thus cannot explicitly capture union-level relation: several previous items affecting the target items jointly. To address this limitation, we propose Multi-Granularity Transformer (MGT) that leverages both point-level and union-level relation for sequential recommendation. The proposed MGT employs a new multi-granularity self-attention (MGSA) mechanism that simultaneously captures multi-level relation (point-level and union-level relation). Specifically, MGSA partitions item latent space into different attention heads and forces different attention heads to account for point-level and union-level relation, respectively. Moreover, to improve the ability of feedforward layer in modeling local patterns, we further propose to incorporate a cross-token scheme into existing point-wise feedforward layer to enable local information interaction between adjacent items. Extensive experiments are conducted on three widely-used benchmark datasets to demonstrate the effectiveness and rationality of the proposed MGT over several state-of-the-art sequential recommendation models. https://www.sciencedirect.com/science/article/abs/pii/S0957417423023102 #MGT #MultiGranularity #Transformer #Leveraging #MultiLevel #Sequential #Multi_Granularity #Multi_Level #Sequential #Self #SelfAttention #Self_Attention #Multi #Level #SA #MGT #MGSA #State_of_the_Art #StateoftheArt @Recommender_Systems

2023 Feature interactive graph neural network for KG-based recommendation Abstract Graph neural network (GNN) is considered as the state-of-art method for KG-based recommendation. However, the existing GNN-based recommendation methods incorporating KG information fail to fully consider interactions between nodes in the process of message passing and aggregating, which will affect the performance improvement of recommendation. To resolve the above limitation, we propose a Feature Interactive Graph Neural Network for KG-based Recommendation (FIKGRec) to explicitly model sophisticated feature interactions from the complex structure of heterogeneous knowledge graph. The overall framework consists of three components: (1) For items, we construct item-KGs where the nodes (entities) denote items and items’ features, and edges represent the relations between entities. Modeling feature interaction can be thus transformed into modeling node (entity) interaction on the knowledge graph. Specifically, we integrate the collaborative signals into the process of KG signals propagation to capture more precise user preferences and then employ the feature entity interaction layer to incorporate the interaction information between entities in the process of neighbor aggregation of entities in item-KG. (2) For users, a preference-aware attention mechanism is designed to obtain the user’s fine-grained preference for items that have been interacted. (3) The final representations of users and items are fed to deep neural network (DNN) to model complex correlations between them. Extensive experiments on three real-world datasets demonstrate the better performance of our FIKGRec framework compared to state-of-the-arts methods #GNN #Feature #FeatureInteractions #Feature_Interactions #KnowledgeGraph #Knowledge_Graph #Knowledge #Graph #NeuralNetwork #Neural #Network #AttentionMechanism #Attention_Mechanism #Attention #Mechanism #KGBased #KG #FIKGRec #DNN @Recommender_Systems

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2024 D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems 🔗 https://arxiv.org/pdf/2401.11478.pdf ABSTRACT A vast amount of user behavior data is constantly accumulating on today’s large recommendation platforms, recording users’ various interests and tastes. Preserving knowledge from the old data while new data continually arrives is a vital problem for recommender systems. Existing approaches generally seek to save the knowledge implicitly in the model parameters. However, such a parameter centric approach lacks scalability and flexibility—the capacity is hard to scale, and the knowledge is inflexible to utilize. Hence, in this work, we propose a framework that turns massive user behavior data to retrievable knowledge (D2K). It is a data-centric approach that is model-agnostic and easy to scale up. Different from only storing unary knowledge such as the user-side or item-side information, D2K proposes to store ternary knowledge for recommendation, which is determined by the complete recommendation factors— user, item, and context. The knowledge retrieved by target samples can be directly used to enhance the performance of any recommendation algorithms. Specifically, we introduce a Transformer-based knowledge encoder to transform the old data into knowledge with the user-item-context cross features. A personalized knowledge adaptation unit is devised to effectively exploit the information from the knowledge base by adapting the retrieved knowledge to the target samples. Extensive experiments on two public datasets show that D2K significantly outperforms existing baselines and is compatible with a major collection of recommendation algorithms. #D2K #Historical #Data #DataCentric #Data_Centric #Centric #Retrievable #Knowledge #Scale #Framework #Behavior #UserBehavior #User_Behavior #User #Item #Context #Transformer #Personalized @Recommender_Systems