Proceedings of the 13th ACM Conference on Recommender Systems最新文献

{"title":"Efficient similarity computation for collaborative filtering in dynamic environments","authors":"Olivier Jeunen, Koen Verstrepen, Bart Goethals","doi":"10.1145/3298689.3347017","DOIUrl":"https://doi.org/10.1145/3298689.3347017","url":null,"abstract":"The problem of computing all pairwise similarities in a large collection of vectors is a well-known and common data mining task. As the number and dimensionality of these vectors keeps increasing, however, currently existing approaches are often unable to meet the strict efficiency requirements imposed by the environments they need to perform in. Real-time neighbourhood-based collaborative filtering (CF) is one example of such an environment in which performance is critical. In this work, we present a novel algorithm for efficient and exact similarity computation between sparse, high-dimensional vectors. Our approach exploits the sparsity that is inherent to implicit feedback data-streams, entailing significant gains compared to other methods. Furthermore, as our model learns incrementally, it is naturally suited for dynamic real-time CF environments. We propose a MapReduce-inspired parallellisation procedure along with our method, and show how even more speed-up can be achieved. Additionally, in many real-world systems, many items are actually not recommendable at any given time, due to recency, stock, seasonality, or enforced business rules. We exploit this fact to further improve the computational efficiency of our approach. Experimental evaluation on both real-world and publicly available datasets shows that our approach scales up to millions of processed user-item interactions per second, and well advances the state-of-the-art.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"37 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":"127646951","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}

{"title":"Attribute-aware non-linear co-embeddings of graph features","authors":"Ahmed Rashed, Josif Grabocka, L. Schmidt-Thieme","doi":"10.1145/3298689.3346999","DOIUrl":"https://doi.org/10.1145/3298689.3346999","url":null,"abstract":"In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation accuracy, especially for users and items that have few ratings. While most recommendation models can be extended to take attributes of users and items into account, their architectures usually become more complicated. While attributes for items are often easy to be provided, attributes for users are often scarce for reasons of privacy or simply because they are not relevant to the operational process at hand. In this paper, we address these two problems for attribute-aware recommender systems by proposing a simple model that co-embeds users and items into a joint latent space in a similar way as a vanilla matrix factorization, but with non-linear latent features construction that seamlessly can ingest user or item attributes or both (GraphRec). To address the second problem, scarce attributes, the proposed model treats the user-item relation as a bipartite graph and constructs generic user and item attributes via the Laplacian of the user-item co-occurrence graph that requires no further external side information but the mere rating matrix. In experiments on three recommender datasets, we show that GraphRec significantly outperforms existing state-of-the-art attribute-aware and content-aware recommender systems even without using any side information.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"3 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":"124438766","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}

{"title":"StoryTime: eliciting preferences from children for book recommendations","authors":"Ashlee Milton, Michael Green, Adam Keener, Joshua Ames, Michael D. Ekstrand, M. S. Pera","doi":"10.1145/3298689.3347048","DOIUrl":"https://doi.org/10.1145/3298689.3347048","url":null,"abstract":"We present StoryTime, a book recommender for children. Our web-based recommender is co-designed with children and uses images to elicit their preferences. By building on existing solutions related to both visual interfaces and book recommendation strategies for children, StoryTime can generate suggestions without historical data or adult guidance. We discuss the benefits of StoryTime as a starting point for further research exploring the cold start problem, incorporating historical data, and needs related to children as a complex audience to enhance the recommendation process.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"69 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":"121505222","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}

{"title":"ORSUM 2019 2nd workshop on online recommender systems and user modeling","authors":"João Vinagre, A. Jorge, A. Bifet, Marie Al-Ghossein","doi":"10.1145/3298689.3347057","DOIUrl":"https://doi.org/10.1145/3298689.3347057","url":null,"abstract":"The ever-growing nature of user generated data in online systems poses obvious challenges on how we process such data. Typically, this issue is regarded as a scalability problem and has been mainly addressed with distributed algorithms able to train on massive amounts of data in short time windows. However, data is inevitably adding up at high speeds. Eventually one needs to discard or archive some of it. Moreover, the dynamic nature of data in user modeling and recommender systems, such as change of user preferences, and the continuous introduction of new users and items make it increasingly difficult to maintain up-to-date, accurate recommendation models. The objective of this workshop is to bring together researchers and practitioners interested in incremental and adaptive approaches to stream-based user modeling, recommendation and personalization, including algorithms, evaluation issues, incremental content and context mining, privacy and transparency, temporal recommendation or software frameworks for continuous learning.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"43 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":"122445726","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}

{"title":"Using AI to build communities around interests on LinkedIn","authors":"Abdulla Al-Qawasmeh, Ankan Saha","doi":"10.1145/3298689.3346976","DOIUrl":"https://doi.org/10.1145/3298689.3346976","url":null,"abstract":"At LinkedIn, our mission is to connect the world's professionals to make them more productive and successful. Our team, Communities Artificial Intelligence (AI), at LinkedIn helps our members achieve this goal is by providing a platform where communities can form around common interests and shared experiences. Fostering active communities at LinkedIn can be broken down into the following components: (1) Discover: Help members find new entities (members, companies, hashtags, and more) to follow that will expose them to communities that share their interests. (2) Engage: Engage members in the conversations taking place in their communities by recommending content from their areas of interest. (3) Contribute: Help members effectively engage with the right communities when they create or share content. These three components form the main pillars of a content-driven ecosystem and our goal is to use AI to successfully close the loop between Discover (via providing relevant follow recommendations), Engage (via delivering engaging content to users from their areas of interest), and Contribute (via suggesting hashtags to content creators to target the right audience). A diverse set of AI techniques is required to address the challenges that arise in each of these components. These techniques include: Supervised Learning (XGBoost, Logistic Regression, Linear Regression), Wide and Deep Models, Natural Language Processing (e.g., Word Embeddings, ngram matching), and Unsupervised Learning. In this presentation, we will provide an overview of the AI techniques we use to form active communities on LinkedIn. We will describe two solutions in detail. First, we will describe how we have built our Follow Recommendations product. The goal of the Follow Recommendations product is to recommend entities to a member that the member finds both immediately relevant (i.e., increase the probability the member will follow the recommended entity) as well as engaging in the long run (i.e., the recommended entity produces content that the member finds relevant). Our analysis of the performance of our follow recommendations models has shown the superiority of nonlinear models compared to their linear counterparts. To manage the explosion of data emanating from terabytes of features generated from (viewer, entity) pairs, we use an innovative 2-D hash join algorithm that was developed at LinkedIn. We are also moving towards a hybrid scoring architecture. This allows us to score candidates with complex offline models and then re-rank these candidates based on more time-sensitive contextual features online. This generates more relevant and timely recommendations for the members based on their recent activity on different parts of the LinkedIn ecosystem. Second, we will describe our approach to solve the problem of Hashtag Suggestion and Typeahead. Hashtags are a great tool that allows members to expand the reach of their posts to the right audience (or communities). Our Hashtag Sug","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"37 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":"124093776","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}

{"title":"Groupon finally explains why we showed those offers","authors":"Sasank Channapragada, Harshit Syal, Ibrahim Maali","doi":"10.1145/3298689.3346979","DOIUrl":"https://doi.org/10.1145/3298689.3346979","url":null,"abstract":"Groupon has a large inventory of offers as varied as local taquerias, massages, concert tickets, and trips to Costa Rica. Our Search & Recommendations team continues to develop algorithmic recommendations systems, machine-learned query understanding models, and increasingly sophisticated personalization and sales conversion estimations. Across an inventory of millions of offers, including many highly localized and geographically-specific ones unique to Groupon's Local business, we strive to balance inventory exploration and matching our users with the exact right item. Our Recommendations models take a variety of factors into account so that we can make the most relevant suggestions to our customers in their neighborhood, or while traveling in one of our hundreds of domestic and international markets. Our system must index millions of items, including the many specific to a user's location; score the deals based on estimated conversion; and finally make adjustments for personalization, exploration, and diversity before delivering our ranked list of inventory to the platform. Yet despite our efforts, many of our customers are unaware of how highly considered their Groupon App and Emails are. In numerous customer interviews we found a huge perception gap that had to be addressed. Customers expressed that our central scrollable home feed felt \"cluttered\", \"disorganized\", and \"like a garage sale\". It was clear to us that the next great sophisticated recommendation feature meant nothing if our customers couldn't appreciate it. Collectively, we realized that we were missing a key communication with our customers. Customers of large internet marketplaces-whether eCommerce, Social Media, or Digital Media-have become accustomed to explanations or qualifications for the recommendations being shown to them. These often take the form of widgets or collections/carousels with titles that explain the grouping such as: \"Because you watched \"Pulp Fiction\" or \"Your friend liked this post by Cardi B\". Our team decided we could demonstrate our own consideration logic to customers, explain the reasoning of their deal feed, and hopefully encourage them to interact and personalize their experience more. Because of the amount of data being considered to drive our recommendations, our team had to develop a system which could generate multiple personalized explanations, score them, and budget the various messages with the deal feed.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"59 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":"115517224","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}

{"title":"Collective embedding for neural context-aware recommender systems","authors":"F. Costa, Peter Dolog","doi":"10.1145/3298689.3347028","DOIUrl":"https://doi.org/10.1145/3298689.3347028","url":null,"abstract":"Context-aware recommender systems consider contextual features as additional information to predict user's preferences. For example, the recommendations could be based on time, location, or the company of other people. Among the contextual information, time became an important feature because user preferences tend to change over time or be similar in the near future. Researchers have proposed different models to incorporate time into their recommender system, however, the current models are not able to capture specific temporal patterns. To address the limitation observed in previous works, we propose Collective embedding for Neural Context-Aware Recommender Systems (CoNCARS). The proposed solution jointly model the item, user and time embeddings to capture temporal patterns. Then, CoNCARS use the outer product to model the user-item-time correlations between dimensions of the embedding space. The hidden features feed our Convolutional Neural Networks (CNNs) to learn the non-linearities between the different features. Finally, we combine the output from our CNNs in the fusion layer and then predict the user's preference score. We conduct extensive experiments on real-world datasets, demonstrating CoNCARS improves the top-N item recommendation task and outperform the state-of-the-art recommendation methods.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"26 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":"114191259","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}

{"title":"Quick and accurate attack detection in recommender systems through user attributes","authors":"Mehmet Aktukmak, Y. Yilmaz, Ismail Uysal","doi":"10.1145/3298689.3347050","DOIUrl":"https://doi.org/10.1145/3298689.3347050","url":null,"abstract":"Malicious profiles have been a credible threat to collaborative recommender systems. Attackers provide fake item ratings to systematically manipulate the platform. Attack detection algorithms can identify and remove such users by observing rating distributions. In this study, we aim to use the user attributes as an additional information source to improve the accuracy and speed of attack detection. We propose a probabilistic factorization model which can embed mixed data type user attributes and observed ratings into a latent space to generate anomaly statistics for new users. To identify the persistent outliers in the system, we also propose a sequential attack detection algorithm to enable quick and accurate detection based on the probabilistic model learned from genuine users. The proposed model demonstrates significant improvements in both accuracy and speed when compared to baseline algorithms on a popular benchmark dataset.","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":"115995656","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}

{"title":"PDMFRec","authors":"Erika Duriakova, E. Tragos, Barry Smyth, Neil Hurley, Francisco J. Peña, Panagiotis Symeonidis, James Geraci, A. Lawlor","doi":"10.1145/3298689.3347035","DOIUrl":"https://doi.org/10.1145/3298689.3347035","url":null,"abstract":"Conventional approaches to matrix factorisation (MF) typically rely on a centralised collection of user data for building a MF model. This approach introduces an increased risk when it comes to user privacy. In this short paper we propose an alternative, user-centric, privacy enhanced, decentralised approach to MF. Our method pushes the computation of the recommendation model to the user's device, and eliminates the need to exchange sensitive personal information; instead only the loss gradients of local (device-based) MF models need to be shared. Moreover, users can select the amount and type of information to be shared, for enhanced privacy. We demonstrate the effectiveness of this approach by considering different levels of user privacy in comparison with state-of-the-art alternatives.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"140 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":"115475867","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}

{"title":"Users in the loop: a psychologically-informed approach to similar item retrieval","authors":"Amy A. Winecoff, Florin Brasoveanu, Bryce Casavant, Pearce Washabaugh, Matthew Graham","doi":"10.1145/3298689.3347047","DOIUrl":"https://doi.org/10.1145/3298689.3347047","url":null,"abstract":"Recommender systems (RS) often leverage information about the similarity between items' features to make recommendations. Yet, many commonly used similarity functions make mathematical assumptions such as symmetry (i.e., Sim(a, b) = Sim(b, a)) that are inconsistent with how humans make similarity judgments. Moreover, most algorithm validations either do not directly measure users' behavior or fail to comply with methodological standards for psychological research. RS that are developed and evaluated without regard to users' psychology may fail to meet users' needs. To provide recommendations that do meet the needs of users, we must: 1) develop similarity functions that account for known properties of human cognition, and 2) rigorously evaluate the performance of these functions using methodologically sound user testing. Here, we develop a framework for evaluating users' judgments of similarity that is informed by best practices in psychological research methods. Employing users' fashion item similarity judgments collected using our framework, we demonstrate that a psychologically-informed similarity function (i.e., Tversky contrast model) outperforms a psychologically-naive similarity function (i.e., Jaccard similarity) in predicting users' similarity judgments.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"44 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":"123369305","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}