276 research outputs found
Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender
There are roughly three stages in real industrial recommendation systems,
candidates generation (retrieval), ranking and reranking. Individual-level
diversity and system-level diversity are both important for industrial
recommender systems. The former focus on each single user's experience, while
the latter focus on the difference among users. Graph-based retrieval
strategies are inevitably hijacked by heavy users and popular items, leading to
the convergence of candidates for users and the lack of system-level diversity.
Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is
deployed to increase individual-level diverisity. Heavily relying on the
semantic information of items, DPP suffers from clickbait and inaccurate
attributes. Besides, most studies only focus on one of the two levels of
diversity, and ignore the mutual influence among different stages in real
recommender systems. We argue that individual-level diversity and system-level
diversity should be viewed as an integrated problem, and we provide an
efficient and deployable solution for web-scale recommenders. Generally, we
propose to employ the retrieval graph information in diversity-based reranking,
by which to weaken the hidden similarity of items exposed to users, and
consequently gain more graph explorations to improve the system-level
diveristy. Besides, we argue that users' propensity for diversity changes over
time in content feed recommendation. Therefore, with the explored graph, we
also propose to capture the user's real-time personalized propensity to the
diversity. We implement and deploy the combined system in WeChat App's Top
Stories used by hundreds of millions of users. Offline simulations and online
A/B tests show our solution can effectively improve both user engagement and
system revenue
Discrete Conditional Diffusion for Reranking in Recommendation
Reranking plays a crucial role in modern multi-stage recommender systems by
rearranging the initial ranking list to model interplay between items.
Considering the inherent challenges of reranking such as combinatorial
searching space, some previous studies have adopted the evaluator-generator
paradigm, with a generator producing feasible sequences and a evaluator
selecting the best one based on estimated listwise utility. Inspired by the
remarkable success of diffusion generative models, this paper explores the
potential of diffusion models for generating high-quality sequences in
reranking. However, we argue that it is nontrivial to take diffusion models as
the generator in the context of recommendation. Firstly, diffusion models
primarily operate in continuous data space, differing from the discrete data
space of item permutations. Secondly, the recommendation task is different from
conventional generation tasks as the purpose of recommender systems is to
fulfill user interests. Lastly, real-life recommender systems require
efficiency, posing challenges for the inference of diffusion models. To
overcome these challenges, we propose a novel Discrete Conditional Diffusion
Reranking (DCDR) framework for recommendation. DCDR extends traditional
diffusion models by introducing a discrete forward process with tractable
posteriors, which adds noise to item sequences through step-wise discrete
operations (e.g., swapping). Additionally, DCDR incorporates a conditional
reverse process that generates item sequences conditioned on expected user
responses. Extensive offline experiments conducted on public datasets
demonstrate that DCDR outperforms state-of-the-art reranking methods.
Furthermore, DCDR has been deployed in a real-world video app with over 300
million daily active users, significantly enhancing online recommendation
quality
Evaluating Recommender Systems Qualitatively: A survey and Comparative Analysis
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsRecommender systems have improved users' online quality of life by helping them find interesting and
valuable items within a large item set. Most recommender system validation research has focused on
accuracy metrics, studying the differences between the predicted and actual user ratings. However,
recent research has found accuracy to underperform when systems go live, mainly due to accuracy’s
inability to validate recommendation lists as a single entity, and shifted to evaluating recommender
systems using "beyond-accuracy" metrics, like novelty and diversity.
In this dissertation, we summarize and organize the leading research regarding the definitions and
objectives of the beyond-accuracy metrics. Such metrics include coverage, diversity, novelty,
serendipity, unexpectedness, utility, and fairness. The behaviors and relationships of these metrics are
analyzed using four different models, two concerning the items characteristics (item-based) and two
regarding the user behaviors (user-based). Furthermore, a new metric is proposed that allows the
comparison of different models considering their overall beyond-accuracy performance. Using this
metric, a reraking approach is designed to improve the performance of a system, aiming to achieve
better recommendations. The impact of the reranking technique on each metric and algorithm is
studied, and the accuracy and non-accuracy performance of each system is compared. We realized
that, although the reranking technique can increase most beyond-accuracy metrics, the accuracy of
that system starts to worsen due to the negative correlation between these two dimensions. We also
found that item-based models tend to achieve much lower values of coverage and diversity than userbased models
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