Proceedings of the 14th ACM Conference on Recommender Systems最新文献

“You Really Get Me”: Conversational AI Agents That Can Truly Understand and Help Users “你真的懂我”:能够真正理解和帮助用户的对话式人工智能代理

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3418436

Michelle X. Zhou

引用次数: 0

Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation 增强推荐的内容协同解纠缠表示学习

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3412239

Yin Zhang, Ziwei Zhu, Yun He, James Caverlee

{"title":"Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation","authors":"Yin Zhang, Ziwei Zhu, Yun He, James Caverlee","doi":"10.1145/3383313.3412239","DOIUrl":"https://doi.org/10.1145/3383313.3412239","url":null,"abstract":"Modern recommenders usually consider both collaborative features from user behavior data (e.g., clicks) and content information about the users and items (e.g., user ages or item images) for improved recommendations. While encouraging, the uncovered user preference representations derived from these collaborative and content-based perspectives can be entangled by intermixing the influence from each other, leading to sub-optimal performance and unstable recommendations. Hence, we propose to disentangle representations learned from user behavior data and content information. Specifically, we propose a novel two-level disentanglement generative recommendation model (DICER) that supports both content-collaborative disentanglement and feature disentanglement: for the content-collaborative disentanglement, DICER decomposes the features by their marginal distributions based on content and user-item interactions, to ensure the learned features from each type are statistically independent. For feature disentanglement, by decomposing the Kullback-Leibler divergence, we theoretically show that extracted features within each type are disentangled at a granular level. Furthermore, DICER utilizes a co-decoder that simultaneously decodes the content and user-item interactions to ensure the high-quality of learned features. Through extensive experiments on three real-world datasets, results show that DICER significantly outperforms other state-of-the-art methods by 13.5% in NDCG and 14.4% in hit ratio on average.","PeriodicalId":310522,"journal":{"name":"Proceedings of the 14th ACM Conference on Recommender Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127235925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de 推荐下一个观看的视频:YOUTV.de的离线和在线评估

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3412257

P. Symeonidis, Andrea Janes, Dmitry Chaltsev, Philip Giuliani, Daniel Morandini, A. Unterhuber, Ludovik Çoba, M. Zanker

{"title":"Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de","authors":"P. Symeonidis, Andrea Janes, Dmitry Chaltsev, Philip Giuliani, Daniel Morandini, A. Unterhuber, Ludovik Çoba, M. Zanker","doi":"10.1145/3383313.3412257","DOIUrl":"https://doi.org/10.1145/3383313.3412257","url":null,"abstract":"The task “recommend a video to watch next?” has been in the focus of recommender systems’ research for a long time. However, adequately exploiting the clues hidden in the sequences of actions of user sessions in order to reveal users’ short-term intentions moved only recently into the focus of research. Based on a real-world application scenario, in this paper, we propose a Markov Chain-based transition probability matrix to efficiently reveal the short-term preferences of individuals. We experimentally evaluated our proposed method by comparing it against state-of-the-art algorithms in an offline as well as a live evaluation setting. In both cases our method not only demonstrated its superiority over its competitors, but exposed a clearly stronger engagement of users on the platform. In the online setting, our method improved the click-through rate by up to 93.61%. This paper therefore contributes real-world evidence for improving the recommendation effectiveness, by considering sequence-awareness, since capturing the short-term preferences of users is crucial in the light of items with a short life span such as tv programs (news, tv shows, etc.).","PeriodicalId":310522,"journal":{"name":"Proceedings of the 14th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115990208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Closed-Form Models for Collaborative Filtering with Side-Information 边信息协同过滤的封闭模型

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3418480

Olivier Jeunen, J. V. Balen, Bart Goethals

引用次数: 10

Conversational Agents for Recommender Systems 推荐系统的会话代理

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3411453

Andrea Iovine

引用次数: 1

A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems 会话推荐系统潜在线性评论的排序优化方法

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3412240

Hanze Li, S. Sanner, Kai Luo, Ga Wu

{"title":"A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems","authors":"Hanze Li, S. Sanner, Kai Luo, Ga Wu","doi":"10.1145/3383313.3412240","DOIUrl":"https://doi.org/10.1145/3383313.3412240","url":null,"abstract":"Critiquing is a method for conversational recommendation that incrementally adapts recommendations in response to user preference feedback. Specifically, a user is iteratively provided with item recommendations and attribute descriptions for those items; the user may then either accept the recommendation or choose to critique an attribute to generate a new recommendation. A recent direction known as latent linear critiquing (LLC) takes a modern embedding-based approach that seeks to optimize the combination of user preference embeddings with embeddings of critiques based on subjective item descriptions (i.e., keyphrases from user reviews); LLC does so by exploiting the linear structure of the embeddings to efficiently optimize their weights in a linear programming (LP) formulation. In this paper, we revisit LLC and note that it’s score-based optimization approach inherently encourages extreme weightings in order to maximize predicted score gaps between preferred and non-preferred items. Noting that the overall end task objective in critiquing is to re-rank rather than re-score, in this paper we take a ranking optimization approach that seeks to optimize embedding weights based on observed rank violations from earlier critiquing iterations. We evaluate the proposed framework on two recommendation datasets containing user reviews. Empirical results demonstrate that ranking-based LLC generally outperforms scoring-based LLC and other baselines across a variety of datasets, critiquing styles, and both satisfaction and session-length performance metrics.","PeriodicalId":310522,"journal":{"name":"Proceedings of the 14th ACM Conference on Recommender Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124313896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Workshop on Online Misinformation- and Harm-Aware Recommender Systems 在线错误信息和危害意识推荐系统研讨会

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3411537

Antonela Tommasel, D. Godoy, A. Zubiaga

引用次数: 5

Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions 带有点击后转换的排名指标的双鲁棒估计

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3412262

Yuta Saito

{"title":"Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions","authors":"Yuta Saito","doi":"10.1145/3383313.3412262","DOIUrl":"https://doi.org/10.1145/3383313.3412262","url":null,"abstract":"Post-click conversion, a pre-defined action on a web service after a click, is an essential form of feedback, as it directly contributes to the final revenue and accurately captures user preferences for items, compared with the ambiguous click. However, naively using post-click conversions can lead to severe bias when learning or evaluating recommenders because of the selection bias between clicked and unclicked data. In this study, we address the offline evaluation problem of algorithmic recommendations with biased post-click conversions. A possible solution to address this bias is to use the inverse propensity score estimator, as it can provide an unbiased evaluation even with the selection bias. However, this estimator is known to be subject to variance and instability problems, which can be severe in the recommendation setting, as feedback is often highly sparse. To address these limitations with the previous unbiased estimator, we propose a doubly robust estimator for the ground-truth ranking performance of a given recommender. The proposed estimator is unbiased against the ground-truth ranking metric and improves the variance and estimation error tail bound of the existing unbiased estimator. Finally, to evaluate the empirical efficacy of the proposed estimator, we conduct empirical evaluations using semi-synthetic and two public real-world datasets. The results show that the proposed metric reveals a better model evaluation performance compared with existing baseline metrics, particularly in a situation with severe selection bias.","PeriodicalId":310522,"journal":{"name":"Proceedings of the 14th ACM Conference on Recommender Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132511477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 49

Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized Recommendation 基于内容的个性化推荐的自适应点对学习排序

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3412229

Yagmur Gizem Cinar, J. Renders

{"title":"Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized Recommendation","authors":"Yagmur Gizem Cinar, J. Renders","doi":"10.1145/3383313.3412229","DOIUrl":"https://doi.org/10.1145/3383313.3412229","url":null,"abstract":"This paper extends the standard pointwise and pairwise paradigms for learning-to-rank in the context of personalized recommendation, by considering these two approaches as two extremes of a continuum of possible strategies. It basically consists of a surrogate loss that models how to select and combine these two approaches adaptively, depending on the context (query or user, pair of items, etc.). In other words, given a training instance, which is typically a triplet (a query/user and two items with different preferences or relevance grades), the strategy adaptively determines whether it is better to focus on the “most preferred” item (pointwise - positive instance), on the “less preferred” one (pointwise - negative instance) or on the pair (pairwise), or on anything else in between these 3 extreme alternatives. We formulate this adaptive strategy as minimizing a particular loss function that generalizes simultaneously the traditional pointwise and pairwise loss functions (negative log-likelihood) through a mixture coefficient. This coefficient is formulated as a learnable function of the features associated to the triplet. Experimental results on several real-world news recommendation datasets show clear improvements over several pointwise, pairwise, and listwise approaches.","PeriodicalId":310522,"journal":{"name":"Proceedings of the 14th ACM Conference on Recommender Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128419175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Debiasing Item-to-Item Recommendations With Small Annotated Datasets 用小的带注释的数据集消除项目对项目推荐的偏见

Proceedings of the 14th ACM Conference on Recommender Systems Pub Date : 2020-09-22 DOI: 10.1145/3383313.3412265

Tobias Schnabel, Paul N. Bennett

引用次数: 14