60,587 research outputs found
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Graph collaborative filtering, which learns user and item representations
through message propagation over the user-item interaction graph, has been
shown to effectively enhance recommendation performance. However, most current
graph collaborative filtering models mainly construct the interaction graph on
a single behavior domain (e.g. click), even though users exhibit various types
of behaviors on real-world platforms, including actions like click, cart, and
purchase. Furthermore, due to variations in user engagement, there exists an
imbalance in the scale of different types of behaviors. For instance, users may
click and view multiple items but only make selective purchases from a small
subset of them. How to alleviate the behavior imbalance problem and utilize
information from the multiple behavior graphs concurrently to improve the
target behavior conversion (e.g. purchase) remains underexplored. To this end,
we propose IMGCF, a simple but effective model to alleviate behavior data
imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF
utilizes a multi-task learning framework for collaborative filtering on
multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF
improves representation learning on the sparse behavior by leveraging
representations learned from the behavior domain with abundant data volumes.
Experiments on two widely-used multi-behavior datasets demonstrate the
effectiveness of IMGCF.Comment: accepted by ICDM2023 Worksho
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search
In e-commerce search, personalized retrieval is a crucial technique for
improving user shopping experience. Recent works in this domain have achieved
significant improvements by the representation learning paradigm, e.g.,
embedding-based retrieval (EBR) and collaborative filtering (CF). EBR methods
do not sufficiently exploit the useful collaborative signal and are difficult
to learn the representations of long-tail item well. Graph-based CF methods
improve personalization by modeling collaborative signal within the user click
graph. However, existing Graph-based methods ignore user's multiple behaviours,
such as click/purchase and the relevance constraint between user behaviours and
items.In this paper, we propose a Graph Contrastive Learning with
Multi-Objective (GCL-MO) collaborative filtering model, which solves the
problems of weak relevance and incomplete personalization in e-commerce search.
Specifically, GCL-MO builds a homogeneous graph of items and then optimizes a
multi-objective function of personalization and relevance. Moreover, we propose
a modified contrastive loss for multi-objectives graph learning, which avoids
the mutual suppression among positive samples and thus improves the
generalization and robustness of long-tail item representations. These learned
item embeddings are then used for personalized retrieval by constructing an
efficient offline-to-online inverted table. GCL-MO outperforms the online
collaborative filtering baseline in both offline/online experimental metrics
and shows a significant improvement in the online A/B testing of Taobao search
The SECURE collaboration model
The SECURE project has shown how trust can be made computationally tractable while retaining a reasonable connection with human and social notions of trust. SECURE has produced a well-founded theory of trust that has been tested and refined through use in real software such as collaborative spam filtering and electronic purse. The software comprises the SECURE kernel with extensions for policy specification by application developers. It has yet to be applied to large-scale, multi-domain distributed systems taking different application contexts into account. The project has not considered privacy in evidence distribution, a crucial issue for many application domains, including public services such as healthcare and police. The SECURE collaboration model has similarities with the trust domain concept, embodying the interaction set of a principal, but SECURE is primarily concerned with pseudonymous entities rather than domain-structured systems
The Specification of Requirements in the MADAE-Pro Software Process
MADAE-Pro is an ontology-driven process for multi-agent domain and application engineering which promotes the construction and reuse of agent-oriented applications families. This article introduces MADAE-Pro, emphasizing the description of its domain analysis and application requirements engineering phases and showing how software artifacts produced from the first are reused in the last one. Illustrating examples are extracted from two case studies we have conducted to evaluate MADAE-Pro. The first case study assesses the Multi-Agent Domain Engineering sub-process of MADAE-Pro through the development of a multi-agent system family of recommender systems supporting alternative (collaborative, content-based and hybrid) filtering techniques. The second one, evaluates the Multi-Agent Application Engineering sub-process of MADAE-Pro through the construction of InfoTrib, a Tax Law recommender system which provides recommendations based on new tax law information items using a content-based filtering technique. ONTOSERS and InfoTrib were modeled using ONTORMAS, a knowledge-based tool for supporting and automating the tasks of MADAEPro
Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation
Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multi-domain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold-start problem and the hypothesis that cross-domain recommendations provide more accuracy using a large volume of user data from a true multi-domain recommender service. Our results indicate that cross-domain collaborative filtering could significantly improve the quality of recommendation in cold start context and the auxiliary profile size plays an important role in it. © 2013 Springer-Verlag
IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS
Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm.
Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent.
Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance.
To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape
- …