30,668 research outputs found
Rethinking Item Importance in Session-based Recommendation
Session-based recommendation aims to predict users' based on anonymous
sessions. Previous work mainly focuses on the transition relationship between
items during an ongoing session. They generally fail to pay enough attention to
the importance of the items in terms of their relevance to user's main intent.
In this paper, we propose a Session-based Recommendation approach with an
Importance Extraction Module, i.e., SR-IEM, that considers both a user's
long-term and recent behavior in an ongoing session. We employ a modified
self-attention mechanism to estimate item importance in a session, which is
then used to predict user's long-term preference. Item recommendations are
produced by combining the user's long-term preference and current interest as
conveyed by the last interacted item. Experiments conducted on two benchmark
datasets validate that SR-IEM outperforms the start-of-the-art in terms of
Recall and MRR and has a reduced computational complexity
An examination of user-focused context-gathering techniques in recommendation interfaces
Attempts to capture context within applications take a wide variety of forms. While it is generally accepted that a user’s current context shapes how they perceive and interact with a system such as a recommender we here explore a novel method of interacting with the user to gain a conceptual understanding of their own frame of reference. By drawing on a more human-centric approach we show that users accept and participate in sharing of context readily as part of an interactive system
Self Contrastive Learning for Session-based Recommendation
Session-based recommendation, which aims to predict the next item of users'
interest as per an existing sequence interaction of items, has attracted
growing applications of Contrastive Learning (CL) with improved user and item
representations. However, these contrastive objectives: (1) serve a similar
role as the cross-entropy loss while ignoring the item representation space
optimisation; and (2) commonly require complicated modelling, including complex
positive/negative sample constructions and extra data augmentation. In this
work, we introduce Self-Contrastive Learning (SCL), which simplifies the
application of CL and enhances the performance of state-of-the-art CL-based
recommendation techniques. Specifically, SCL is formulated as an objective
function that directly promotes a uniform distribution among item
representations and efficiently replaces all the existing contrastive objective
components of state-of-the-art models. Unlike previous works, SCL eliminates
the need for any positive/negative sample construction or data augmentation,
leading to enhanced interpretability of the item representation space and
facilitating its extensibility to existing recommender systems. Through
experiments on three benchmark datasets, we demonstrate that SCL consistently
improves the performance of state-of-the-art models with statistical
significance. Notably, our experiments show that SCL improves the performance
of two best-performing models by 8.2% and 9.5% in P@10 (Precision) and 9.9% and
11.2% in MRR@10 (Mean Reciprocal Rank) on average across different benchmarks.
Additionally, our analysis elucidates the improvement in terms of alignment and
uniformity of representations, as well as the effectiveness of SCL with a low
computational cost.Comment: Technical Repor
Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review
Background: Recommender systems are information retrieval systems that provide users with relevant items
(e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in
healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing
the cost of healthcare and fostering a healthier lifestyle in the population.
Objective: This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature
published over the past 10 years on the use of health recommender systems for patient interventions. The aim of
this study is to understand the scientific evidence generated about health recommender systems, to identify any
gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, “Ensure healthy
lives and promote well-being for all at all ages”), and to suggest possible reasons for these gaps as well as to
propose some solutions.
Methods: We conducted a scoping review, which consisted of a keyword search of the literature related to health
recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing
Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-lan-guage journal
articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results
simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each
paper in terms of four aspects—the domain, the methodological and procedural aspects, the health promotion
theoretical factors and behavior change theories, and the technical aspects—using a new multidisciplinary
taxonomy.
Results: Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three
features were assessed. The nine features associated with the health promotion theoretical factors and behavior
change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not
assess (cost)-effectiveness.
Discussion: Health recommender systems may be further improved by using relevant behavior change strategies
and by implementing essential characteristics of tailored interventions. In addition, many of the features required
to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects
were not reported in the studies.
Conclusions: The studies analyzed presented few evidence in support of the positive effects of using health recommender
systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should
ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with
electronic health records and the incorporation of health promotion theoretical factors and behavior change
theories. This will render those studies more useful for policymakers since they will cover all aspects needed to
determine their impact toward meeting SDG3.European Union's Horizon 2020 No 68112
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling
In Click-through rate (CTR) prediction models, a user's interest is usually
represented as a fixed-length vector based on her history behaviors. Recently,
several methods are proposed to learn an attentive weight for each user
behavior and conduct weighted sum pooling. However, these methods only manually
select several fields from the target item side as the query to interact with
the behaviors, neglecting the other target item fields, as well as user and
context fields. Directly including all these fields in the attention may
introduce noise and deteriorate the performance. In this paper, we propose a
novel model named AutoAttention, which includes all item/user/context side
fields as the query, and assigns a learnable weight for each field pair between
behavior fields and query fields. Pruning on these field pairs via these
learnable weights lead to automatic field pair selection, so as to identify and
remove noisy field pairs. Though including more fields, the computation cost of
AutoAttention is still low due to using a simple attention function and field
pair selection. Extensive experiments on the public dataset and Tencent's
production dataset demonstrate the effectiveness of the proposed approach.Comment: Accepted by ICDM 202
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Reinforcement learning (RL) has been widely applied in recommendation systems
due to its potential in optimizing the long-term engagement of users. From the
perspective of RL, recommendation can be formulated as a Markov decision
process (MDP), where recommendation system (agent) can interact with users
(environment) and acquire feedback (reward signals).However, it is impractical
to conduct online interactions with the concern on user experience and
implementation complexity, and we can only train RL recommenders with offline
datasets containing limited reward signals and state transitions. Therefore,
the data sparsity issue of reward signals and state transitions is very severe,
while it has long been overlooked by existing RL recommenders.Worse still, RL
methods learn through the trial-and-error mode, but negative feedback cannot be
obtained in implicit feedback recommendation tasks, which aggravates the
overestimation problem of offline RL recommender. To address these challenges,
we propose a novel RL recommender named model-enhanced contrastive
reinforcement learning (MCRL). On the one hand, we learn a value function to
estimate the long-term engagement of users, together with a conservative value
learning mechanism to alleviate the overestimation problem.On the other hand,
we construct some positive and negative state-action pairs to model the reward
function and state transition function with contrastive learning to exploit the
internal structure information of MDP. Experiments demonstrate that the
proposed method significantly outperforms existing offline RL and
self-supervised RL methods with different representative backbone networks on
two real-world datasets.Comment: 11 pages, 7 figure
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