Learning to Recommend Accurate and Diverse Items

Cheng, Peizhe; Wang, Shuaiqiang; Ma, Jun; Sun, Jie; Xiong, Hui

doi:10.1145/3038912.3052585

Cited by 108 publications

(75 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Input: D, ϵ ,L,K Output: θ q , θ s 1 Randomly initialize parameters θ q , θ s ← Uniform(−0.1, 0.1); 2 #Pretraining the S-Network. 3 for j = 1 : K do 4 Sample random mini-batches of (s t , i t , r t , s t +1 ) from D; 5 Set f t , d t , r , l t according to s t , r t , s t +1 ; 6 Update θ s via mini-batch SGD w.r.t. the loss in Equation (7) # Updating the Q-Network.…”

Section: Simulator Learningmentioning

confidence: 99%

Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems

Zou

Xia

Ding

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

187

120

View full text Add to dashboard Cite

Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has been widely used in the recommender system, especially on the mobile Apps. The feed streaming setting provides users the interactive manner of recommendation in never-ending feeds. In such a manner, a good recommender system should pay more attention to user stickiness, which is far beyond classical instant metrics and typically measured by long-term user engagement. Directly optimizing long-term user engagement is a non-trivial problem, as the learning target is usually not available for conventional supervised learning methods. Though reinforcement learning (RL) naturally fits the problem of maximizing the long term rewards, applying RL to optimize long-term user engagement is still facing challenges: user behaviors are versatile to model, which typically consists of both instant feedback (e.g., clicks) and delayed feedback (e.g., dwell time, revisit); in addition, performing effective off-policy learning is still immature, especially when combining bootstrapping and function approximation.To address these issues, in this work, we introduce a RL framework -FeedRec to optimize the long-term user engagement. Fee-dRec includes two components: 1) a Q-Network which designed in hierarchical LSTM takes charge of modeling complex user behaviors, and 2) a S-Network, which simulates the environment, assists the Q-Network and voids the instability of convergence in policy learning. Extensive experiments on synthetic data and a real-world large scale data show that FeedRec effectively optimizes the longterm user engagement and outperforms state-of-the-arts. CCS CONCEPTS• Information systems → Recommender systems; Personalization; • Theory of computation → Sequential decision making. * Work performed during an internship at JD.com.

show abstract

Section: Simulator Learningmentioning

confidence: 99%

Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems

Zou

Xia

Ding

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

187

120

View full text Add to dashboard Cite

show abstract

“…CHAMELEON leverages a number of input features to provide more accurate recommendations, as shown in Table 3. In order to understand the effects of including those features in our model, we performed a number of additional experiments with features combined in different Input Configurations (IC) 10 . Table 7 shows five different configurations where we start only with the article IDs (IC1) and incrementally add more features until we have the model with all input features (IC5).…”

Section: ) Analysis Of Additional Quality Factorsmentioning

confidence: 99%

“…Table 8 shows the results of this study. We can generally see that both accuracy (HR@10 and MRR@10) and item 10 This process is sometimes referred to as ablation study. We can also observe cases where measures become lower with the addition of new features.…”

Section: ) Analysis Of Additional Quality Factorsmentioning

confidence: 99%

“…In this setting, the probability p(i) of an item being part of a random user interaction under free discovery is the normalized recent popularity, i.e., p(i) = rec_norm_pop(i), previously presented in (7). In (9), disc(·) is a logarithmic rank discount, defined in (10), that maximizes the impact of novelty for top ranked items, under the assumption that their characteristics will be more visible to users compared to the rest of the top-n recommendation list:…”

Section: A Esi-r@nmentioning

confidence: 99%

“…Typically, most users are not logged in and their short-term reading interests must be estimated from only a few logged interactions, leading to a session-based recommendation problem [5]. Finally, like in certain other application domains, a news RS has to find the right balance between recommending only items with the highest assumed relevance and the diversity and novelty of the recommendations as a whole [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

2019

View full text Add to dashboard Cite

Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a contextual hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty.

show abstract