1,058 research outputs found
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Existing item-based collaborative filtering (ICF) methods leverage only the
relation of collaborative similarity. Nevertheless, there exist multiple
relations between items in real-world scenarios. Distinct from the
collaborative similarity that implies co-interact patterns from the user
perspective, these relations reveal fine-grained knowledge on items from
different perspectives of meta-data, functionality, etc. However, how to
incorporate multiple item relations is less explored in recommendation
research. In this work, we propose Relational Collaborative Filtering (RCF), a
general framework to exploit multiple relations between items in recommender
system. We find that both the relation type and the relation value are crucial
in inferring user preference. To this end, we develop a two-level hierarchical
attention mechanism to model user preference. The first-level attention
discriminates which types of relations are more important, and the second-level
attention considers the specific relation values to estimate the contribution
of a historical item in recommending the target item. To make the item
embeddings be reflective of the relational structure between items, we further
formulate a task to preserve the item relations, and jointly train it with the
recommendation task of preference modeling. Empirical results on two real
datasets demonstrate the strong performance of RCF. Furthermore, we also
conduct qualitative analyses to show the benefits of explanations brought by
the modeling of multiple item relations
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering,
researchers usually make use of side information, such as social networks or
item attributes, to improve recommendation performance. This paper considers
the knowledge graph as the source of side information. To address the
limitations of existing embedding-based and path-based methods for
knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end
framework that naturally incorporates the knowledge graph into recommender
systems. Similar to actual ripples propagating on the surface of water, Ripple
Network stimulates the propagation of user preferences over the set of
knowledge entities by automatically and iteratively extending a user's
potential interests along links in the knowledge graph. The multiple "ripples"
activated by a user's historically clicked items are thus superposed to form
the preference distribution of the user with respect to a candidate item, which
could be used for predicting the final clicking probability. Through extensive
experiments on real-world datasets, we demonstrate that Ripple Network achieves
substantial gains in a variety of scenarios, including movie, book and news
recommendation, over several state-of-the-art baselines.Comment: CIKM 201
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Recent Developments in Recommender Systems: A Survey
In this technical survey, we comprehensively summarize the latest
advancements in the field of recommender systems. The objective of this study
is to provide an overview of the current state-of-the-art in the field and
highlight the latest trends in the development of recommender systems. The
study starts with a comprehensive summary of the main taxonomy of recommender
systems, including personalized and group recommender systems, and then delves
into the category of knowledge-based recommender systems. In addition, the
survey analyzes the robustness, data bias, and fairness issues in recommender
systems, summarizing the evaluation metrics used to assess the performance of
these systems. Finally, the study provides insights into the latest trends in
the development of recommender systems and highlights the new directions for
future research in the field
Neural recommender models for sparse and skewed behavioral data
Modern online platforms offer recommendations and personalized search and services to a large and diverse user base while still aiming to acquaint users with the broader community on the platform. Prior work backed by large volumes of user data has shown that user retention is reliant on catering to their specific eccentric tastes, in addition to providing them popular services or content on the platform.
Long-tailed distributions are a fundamental characteristic of human activity, owing to the bursty nature of human attention. As a result, we often observe skew in data facets that involve human interaction. While there are superficial similarities to Zipf's law in textual data and other domains, the challenges with user data extend further. Individual words may have skewed frequencies in the corpus, but the long-tail words by themselves do not significantly impact downstream text-mining tasks. On the contrary, while sparse users (a majority on most online platforms) contribute little to the training data, they are equally crucial at inference time. Perhaps more so, since they are likely to churn.
In this thesis, we study platforms and applications that elicit user participation in rich social settings incorporating user-generated content, user-user interaction, and other modalities of user participation and data generation. For instance, users on the Yelp review platform participate in a follower-followee network and also create and interact with review text (two modalities of user data). Similarly, community question-answer (CQA) platforms incorporate user interaction and collaboratively authored content over diverse domains and discussion threads. Since user participation is multimodal, we develop generalizable abstractions beyond any single data modality.
Specifically, we aim to address the distributional mismatch that occurs with user data independent of dataset specifics; While a minority of the users generates most training samples, it is insufficient only to learn the preferences of this subset of users. As a result, the data's overall skew and individual users' sparsity are closely interlinked: sparse users with uncommon preferences are under-represented. Thus, we propose to treat these problems jointly with a skew-aware grouping mechanism that iteratively sharpens the identification of preference groups within the user population. As a result, we improve user characterization; content recommendation and activity prediction (+6-22% AUC, +6-43% AUC, +12-25% RMSE over state-of-the-art baselines), primarily for users with sparse activity.
The size of the item or content inventories compounds the skew problem. Recommendation models can achieve very high aggregate performance while recommending only a tiny proportion of the inventory (as little as 5%) to users. We propose a data-driven solution guided by the aggregate co-occurrence information across items in the dataset. We specifically note that different co-occurrences are not equally significant; For example, some co-occurring items are easily substituted while others are not. We develop a self-supervised learning framework where the aggregate co-occurrences guide the recommendation problem while providing room to learn these variations among the item associations. As a result, we improve coverage to ~100% (up from 5%) of the inventory and increase long-tail item recall up to 25%.
We also note that the skew and sparsity problems repeat across data modalities. For instance, social interactions and review content both exhibit aggregate skew, although individual users who actively generate reviews may not participate socially and vice-versa. It is necessary to differentially weight and merge different data sources for each user towards inference tasks in such cases. We show that the problem is inherently adversarial since the user participation modalities compete to describe a user accurately. We develop a framework to unify these representations while algorithmically tackling mode collapse, a well-known pitfall with adversarial models.
A more challenging but important instantiation of sparsity is the few-shot setting or cross-domain setting. We may only have a single or a few interactions for users or items in the sparse domains or partitions. We show that contextualizing user-item interactions helps us infer behavioral invariants in the dense domain, allowing us to correlate sparse participants to their active counterparts (resulting in 3x faster training, ~19% recall gains in multi-domain settings).
Finally, we consider the multi-task setting, where the platform incorporates multiple distinct recommendations and prediction tasks for each user. A single-user representation is insufficient for users who exhibit different preferences along each dimension. At the same time, it is counter-productive to handle correlated prediction or inference tasks in isolation. We develop a multi-faceted representation approach grounded on residual learning with heterogeneous knowledge graph representations, which provides us an expressive data representation for specialized domains and applications with multimodal user data. We achieve knowledge sharing by unifying task-independent and task-specific representations of each entity with a unified knowledge graph framework.
In each chapter, we also discuss and demonstrate how the proposed frameworks directly incorporate a wide range of gradient-optimizable recommendation and behavior models, maximizing their applicability and pertinence to user-centered inference tasks and platforms
Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception
Existing research usually utilizes side information such as social network or
item attributes to improve the performance of collaborative filtering-based
recommender systems. In this paper, the knowledge graph with user perception is
used to acquire the source of side information. We proposed KGUPN to address
the limitations of existing embedding-based and path-based knowledge
graph-aware recommendation methods, an end-to-end framework that integrates
knowledge graph and user awareness into scientific and technological news
recommendation systems. KGUPN contains three main layers, which are the
propagation representation layer, the contextual information layer and
collaborative relation layer. The propagation representation layer improves the
representation of an entity by recursively propagating embeddings from its
neighbors (which can be users, news, or relationships) in the knowledge graph.
The contextual information layer improves the representation of entities by
encoding the behavioral information of entities appearing in the news. The
collaborative relation layer complements the relationship between entities in
the news knowledge graph. Experimental results on real-world datasets show that
KGUPN significantly outperforms state-of-the-art baselines in scientific and
technological news recommendation
Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
The Recommender system is a vital information service on today's Internet.
Recently, graph neural networks have emerged as the leading approach for
recommender systems. We try to review recent literature on graph neural
network-based recommender systems, covering the background and development of
both recommender systems and graph neural networks. Then categorizing
recommender systems by their settings and graph neural networks by spectral and
spatial models, we explore the motivation behind incorporating graph neural
networks into recommender systems. We also analyze challenges and open problems
in graph construction, embedding propagation and aggregation, and computation
efficiency. This guides us to better explore the future directions and
developments in this domain.Comment: arXiv admin note: text overlap with arXiv:2103.08976 by other author
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