Many recommendation algorithms rely on user data to generate recommendations. However, these recommendations also affect the data obtained from future users. This work aims to understand the effects of this dynamic interaction. We propose a simple model where users with heterogeneous preferences arrive over time. Based on this model, we prove that naive estimators, i.e. those which ignore this feedback loop, are not consistent. We show that consistent estimators are efficient in the presence of myopic agents. Our results are validated using extensive simulations.

5
下载
关闭预览

相关内容

IFIP TC13 Conference on Human-Computer Interaction是人机交互领域的研究者和实践者展示其工作的重要平台。多年来,这些会议吸引了来自几个国家和文化的研究人员。官网链接:http://interact2019.org/

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold start. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. On the other hand, we introduce datasets used in these works. Finally, we propose several potential research directions in this field.

0
70
下载
预览

This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based on the items selected by the user. Previous recommendation studies have two limitations: (1) the users who consumed a few items have poor recommendations and (2) inadequate evidence candidates are used to identify user preferences. We propose a meta-learning-based recommender system called MeLU to overcome these two limitations. From meta-learning, which can rapidly adopt new task with a few examples, MeLU can estimate new user's preferences with a few consumed items. In addition, we provide an evidence candidate selection strategy that determines distinguishing items for customized preference estimation. We validate MeLU with two benchmark datasets, and the proposed model reduces at least 5.92% mean absolute error than two comparative models on the datasets. We also conduct a user study experiment to verify the evidence selection strategy.

0
28
下载
预览

This analysis explores the temporal sequencing of objects in a movie trailer. Temporal sequencing of objects in a movie trailer (e.g., a long shot of an object vs intermittent short shots) can convey information about the type of movie, plot of the movie, role of the main characters, and the filmmakers cinematographic choices. When combined with historical customer data, sequencing analysis can be used to improve predictions of customer behavior. E.g., a customer buys tickets to a new movie and maybe the customer has seen movies in the past that contained similar sequences. To explore object sequencing in movie trailers, we propose a video convolutional network to capture actions and scenes that are predictive of customers' preferences. The model learns the specific nature of sequences for different types of objects (e.g., cars vs faces), and the role of sequences in predicting customer future behavior. We show how such a temporal-aware model outperforms simple feature pooling methods proposed in our previous works and, importantly, demonstrate the additional model explain-ability allowed by such a model.

0
5
下载
预览

In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user's historical interactions. With self-attention, it is able to estimate the relative weights of each item in user interaction trajectories to learn better representations for user's transient interests. The model is finally trained in a metric learning framework, taking both short-term and long-term intentions into consideration. Experiments on a wide range of datasets on different domains demonstrate that our approach outperforms the state-of-the-art by a wide margin.

0
4
下载
预览

Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the classical setup where recommendation candidates are evaluated by their coherence with past user behavior, by predicting either the missing entries in the user-item matrix, or the most likely next event. To bridge this gap, we optimize a recommendation policy for the task of increasing the desired outcome versus the organic user behavior. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy. To this end, we propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.

0
17
下载
预览

Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.

0
7
下载
预览

Recommender systems are widely used in big information-based companies such as Google, Twitter, LinkedIn, and Netflix. A recommender system deals with the problem of information overload by filtering important information fragments according to users' preferences. In light of the increasing success of deep learning, recent studies have proved the benefits of using deep learning in various recommendation tasks. However, most proposed techniques only aim to target individuals, which cannot be efficiently applied in group recommendation. In this paper, we propose a deep learning architecture to solve the group recommendation problem. On the one hand, as different individual preferences in a group necessitate preference trade-offs in making group recommendations, it is essential that the recommendation model can discover substitutes among user behaviors. On the other hand, it has been observed that a user as an individual and as a group member behaves differently. To tackle such problems, we propose using an attention mechanism to capture the impact of each user in a group. Specifically, our model automatically learns the influence weight of each user in a group and recommends items to the group based on its members' weighted preferences. We conduct extensive experiments on four datasets. Our model significantly outperforms baseline methods and shows promising results in applying deep learning to the group recommendation problem.

0
11
下载
预览

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although {\em social matrix factorization} (Social MF) and {\em topic matrix factorization} (Topic MF) successfully exploit social relations and item reviews, respectively, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model {\em \mbox{MR3}} to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.

0
5
下载
预览

In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing works. First, most works perform deep content feature learning and resort to matrix factorization, which cannot effectively model the highly complex user-item interaction function. Second, due to the difficulty on training deep neural networks, existing models utilize a shallow architecture, and thus limit the expressive potential of deep learning. Third, neural network models are easy to overfit on the implicit setting, because negative interactions are not taken into account. To tackle these issues, we present a generic recommender framework called Neural Collaborative Autoencoder (NCAE) to perform collaborative filtering, which works well for both explicit feedback and implicit feedback. NCAE can effectively capture the relationship between interactions via a non-linear matrix factorization process. To optimize the deep architecture of NCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, to prevent overfitting on the implicit setting, we propose an error reweighting module and a sparsity-aware data-augmentation strategy. Extensive experiments on three real-world datasets demonstrate that NCAE can significantly advance the state-of-the-art.

0
7
下载
预览

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedbacks. In particular, we introduce an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Moreover, we validate the importance of list-wise recommendations during the interactions between users and agent, and develop a novel approach to incorporate them into the proposed framework LIRD for list-wide recommendations. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

0
12
下载
预览
小贴士
相关论文
Qingyu Guo,Fuzhen Zhuang,Chuan Qin,Hengshu Zhu,Xing Xie,Hui Xiong,Qing He
70+阅读 · 2020年2月28日
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation
Hoyeop Lee,Jinbae Im,Seongwon Jang,Hyunsouk Cho,Sehee Chung
28+阅读 · 2019年7月31日
Cheng-Kang Hsieh,Miguel Campo,Abhinav Taliyan,Matt Nickens,Mitkumar Pandya,JJ Espinoza
5+阅读 · 2018年10月18日
Next Item Recommendation with Self-Attention
Shuai Zhang,Yi Tay,Lina Yao,Aixin Sun
4+阅读 · 2018年8月25日
Stephen Bonner,Flavian Vasile
17+阅读 · 2018年8月3日
Han Zhu,Xiang Li,Pengye Zhang,Guozheng Li,Jie He,Han Li,Kun Gai
7+阅读 · 2018年5月21日
Tran Dang Quang Vinh,Tuan-Anh Nguyen Pham,Gao Cong,Xiao-Li Li
11+阅读 · 2018年4月18日
Guang-Neng Hu,Xin-Yu Dai,Feng-Yu Qiu,Rui Xia,Tao Li,Shu-Jian Huang,Jia-Jun Chen
5+阅读 · 2018年3月26日
Qibing Li,Xiaolin Zheng,Xinyue Wu
7+阅读 · 2018年1月30日
Xiangyu Zhao,Liang Zhang,Zhuoye Ding,Dawei Yin,Yihong Zhao,Jiliang Tang
12+阅读 · 2018年1月5日
相关VIP内容
Keras François Chollet 《Deep Learning with Python 》, 386页pdf
专知会员服务
49+阅读 · 2019年10月12日
2019年机器学习框架回顾
专知会员服务
22+阅读 · 2019年10月11日
[综述]深度学习下的场景文本检测与识别
专知会员服务
31+阅读 · 2019年10月10日
相关资讯
LibRec 精选:AutoML for Contextual Bandits
LibRec智能推荐
6+阅读 · 2019年9月19日
LibRec 精选:CCF TPCI 的推荐系统专刊征稿
LibRec智能推荐
4+阅读 · 2019年1月12日
LibRec 精选:推荐系统的论文与源码
LibRec智能推荐
9+阅读 · 2018年11月29日
LibRec 精选:基于LSTM的序列推荐实现(PyTorch)
LibRec智能推荐
34+阅读 · 2018年8月27日
LibRec 精选:推荐的可解释性[综述]
LibRec智能推荐
8+阅读 · 2018年5月4日
LibRec 精选:推荐系统9个必备数据集
LibRec智能推荐
4+阅读 · 2018年3月7日
推荐|深度强化学习聊天机器人(附论文)!
全球人工智能
4+阅读 · 2018年1月30日
分布式TensorFlow入门指南
机器学习研究会
4+阅读 · 2017年11月28日
【推荐】用Tensorflow理解LSTM
机器学习研究会
26+阅读 · 2017年9月11日
Top