Proceedings of the 13th ACM Conference on Recommender Systems最新文献_第3页

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Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346983

Amarjit Saini, Florin Rusu, Andrew Johnston

引用次数: 1

Uplift-based evaluation and optimization of recommenders 基于提升的推荐人评价与优化

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347018

Masahiro Sato, Janmajay Singh, S. Takemori, Takashi Sonoda, Qian Zhang, T. Ohkuma

{"title":"Uplift-based evaluation and optimization of recommenders","authors":"Masahiro Sato, Janmajay Singh, S. Takemori, Takashi Sonoda, Qian Zhang, T. Ohkuma","doi":"10.1145/3298689.3347018","DOIUrl":"https://doi.org/10.1145/3298689.3347018","url":null,"abstract":"Recommender systems aim to increase user actions such as clicks and purchases. Typical evaluations of recommenders regard the purchase of a recommended item as a success. However, the item may have been purchased even without the recommendation. An uplift is defined as an increase in user actions caused by recommendations. Situations with and without a recommendation cannot both be observed for a specific user-item pair at a given time instance, making uplift-based evaluation and optimization challenging. This paper proposes new evaluation metrics and optimization methods for the uplift in a recommender system. We apply a causal inference framework to estimate the average uplift for the offline evaluation of recommenders. Our evaluation protocol leverages both purchase and recommendation logs under a currently deployed recommender system, to simulate the cases both with and without recommendations. This enables the offline evaluation of the uplift for newly generated recommendation lists. For optimization, we need to define positive and negative samples that are specific to an uplift-based approach. For this purpose, we deduce four classes of items by observing purchase and recommendation logs. We derive the relative priorities among these four classes in terms of the uplift and use them to construct both pointwise and pairwise sampling methods for uplift optimization. Through dedicated experiments with three public datasets, we demonstrate the effectiveness of our optimization methods in improving the uplift.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123127628","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: 30

FineNet

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346968

Yu-Che Tsai, Chih-Yao Chen, Shao-Lun Ma, Pei-Chi Wang, You-Jia Chen, Yu-Chieh Chang, Cheng-te Li

引用次数: 8

From preference into decision making: modeling user interactions in recommender systems 从偏好到决策:在推荐系统中建模用户交互

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347065

Qian Zhao, M. Willemsen, G. Adomavicius, F. M. Harper, J. Konstan

引用次数: 6

Enhancing VAEs for collaborative filtering: flexible priors & gating mechanisms 增强协同过滤的vae:灵活的先验和门控机制

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347015

Daeryong Kim, B. Suh

{"title":"Enhancing VAEs for collaborative filtering: flexible priors & gating mechanisms","authors":"Daeryong Kim, B. Suh","doi":"10.1145/3298689.3347015","DOIUrl":"https://doi.org/10.1145/3298689.3347015","url":null,"abstract":"Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model's inability to learn deeper representations with more than one hidden layer for each network. Our goal is to incorporate appropriate techniques to mitigate the aforementioned problems of variational autoencoder CF and further improve the recommendation performance. Our work is the first to apply flexible priors to collaborative filtering and show that simple priors (in original VAEs) may be too restrictive to fully model user preferences and setting a more flexible prior gives significant gains. We experiment with the VampPrior, originally proposed for image generation, to examine the effect of flexible priors in CF. We also show that VampPriors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 2 popular benchmark datasets (MovieLens & Netflix).","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128042527","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: 34

User-centric evaluation of session-based recommendations for an automated radio station 以用户为中心的基于会话的自动化无线电台建议评估

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347046

Malte Ludewig, D. Jannach

{"title":"User-centric evaluation of session-based recommendations for an automated radio station","authors":"Malte Ludewig, D. Jannach","doi":"10.1145/3298689.3347046","DOIUrl":"https://doi.org/10.1145/3298689.3347046","url":null,"abstract":"The creation of an automated and virtually endless playlist given a start item is a common feature of modern media streaming services. When no past information about the user's preferences is available, the creation of such playlists can be done using session-based recommendation techniques. In this case, the recommendations only depend on the start item and the user's interactions in the current listening session, such as \"liking\" or skipping an item. In recent years, various novel session-based techniques were proposed, often based on deep learning. The evaluation of such approaches is in most cases solely based on offline experimentation and abstract accuracy measures. However, such evaluations cannot inform us about the quality as perceived by users. To close this research gap, we have conducted a user study (N=250), where the participants interacted with an automated online radio station. Each treatment group received recommendations that were generated by one of five different algorithms. Our results show that comparably simple techniques led to quality perceptions that are similar or even better than when a complex deep learning mechanism or Spotify's recommendations are used. The simple mechanisms, however, often tend to recommend comparably popular tracks, which can lead to lower discovery effects.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"416 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792929","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: 9

SMORe: modularize graph embedding for recommendation SMORe:模块化图嵌入推荐

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346953

Chih-Ming Chen, Ting-Hsiang Wang, Chuan-Ju Wang, Ming-Feng Tsai

{"title":"SMORe: modularize graph embedding for recommendation","authors":"Chih-Ming Chen, Ting-Hsiang Wang, Chuan-Ju Wang, Ming-Feng Tsai","doi":"10.1145/3298689.3346953","DOIUrl":"https://doi.org/10.1145/3298689.3346953","url":null,"abstract":"In the Age of Big Data, graph embedding has received increasing attention for its ability to accommodate the explosion in data volume and diversity, which challenge the foundation of modern recommender systems. Respectively, graph facilitates fusing complex systems of interactions into a unified structure and distributed embedding enables efficient retrieval of entities, as in the case of approximate nearest neighbor (ANN) search. When combined, graph embedding captures relational information beyond entity interaction and towards a problem's underlying structure, as epitomized by struct2vec [20] and PinSage [26]. This session will start by brushing up on the basics about graphs and embedding methods and discussing their merits. We then quickly dive into using the mathematical formulation of graph embedding to derive the modular framework: Sampler-Mapper-Optimizer for Recommendation, or SMORe. We demonstrate existing models used for recommendation, such as MF and BPR, can all be assembled using three basic components: sampler, mapper, and optimizer. The tutorial is accompanied by a hands-on session, where we show how graph embedding can model complex systems through the multi-task learning and the cross-platform data sparsity alleviation tasks.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115693823","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: 3

Efficient privacy-preserving recommendations based on social graphs 基于社交图谱的高效隐私保护建议

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347013

A. Wainakh, Tim Grube, Jörg Daubert, M. Mühlhäuser

引用次数: 13

The influence of personal values on music taste: towards value-based music recommendations 个人价值观对音乐品味的影响:基于价值观的音乐推荐

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347021

Sandy Manolios, A. Hanjalic, Cynthia C. S. Liem

{"title":"The influence of personal values on music taste: towards value-based music recommendations","authors":"Sandy Manolios, A. Hanjalic, Cynthia C. S. Liem","doi":"10.1145/3298689.3347021","DOIUrl":"https://doi.org/10.1145/3298689.3347021","url":null,"abstract":"The field of recommender systems has a lot to gain from the field of psychology. Indeed, many psychology researchers have investigated relations between models that describe humans and consumption preferences. One example of this is personality, which has been shown to be a valid construct to describe people. As a consequence, personality-based recommenders have already proven to be a lead toward improving recommendations, by adapting them to their users' traits. Beyond personality, there are more ways to describe a person's identity. One of these ways is to consider personal values: what is important for the users in life at the most abstract level. Being complementary to personality traits, values may give another lead towards better user understanding. In this paper, we investigate this, taking music as a use case. We use a marketing interview technique to elicit 22 users' personal values connected to their musical preferences. We show that personal values indeed play a role in people's music preferences, and are the first to propose a map linking personal values to music preferences. We see this map as a first step in devising a value-based user model for music recommender systems.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123423925","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: 13

A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation 电子商务推荐中多目标优化的pareto高效算法

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346998

Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, Peng Jiang

{"title":"A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation","authors":"Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, Peng Jiang","doi":"10.1145/3298689.3346998","DOIUrl":"https://doi.org/10.1145/3298689.3346998","url":null,"abstract":"Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no single objective can be further improved without hurting the others. However existing approaches to Pareto efficient multi-objective recommendation still lack good theoretical guarantees. In this paper, we propose a general framework for generating Pareto efficient recommendations. Assuming that there are formal differentiable formulations for the objectives, we coordinate these objectives with a weighted aggregation. Then we propose a condition ensuring Pareto efficiency theoretically and a two-step Pareto efficient optimization algorithm. Meanwhile the algorithm can be easily adapted for Pareto Frontier generation and fair recommendation selection. We specifically apply the proposed framework on E-Commerce recommendation to optimize GMV and CTR simultaneously. Extensive online and offline experiments are conducted on the real-world E-Commerce recommender system and the results validate the Pareto efficiency of the framework. To the best of our knowledge, this work is among the first to provide a Pareto efficient framework for multi-objective recommendation with theoretical guarantees. Moreover, the framework can be applied to any other objectives with differentiable formulations and any model with gradients, which shows its strong scalability.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125549311","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: 88