Proceedings of the Eleventh ACM Conference on Recommender Systems最新文献

{"title":"Search Ranking And Personalization at Airbnb","authors":"Mihajlo Grbovic","doi":"10.1145/3109859.3109920","DOIUrl":"https://doi.org/10.1145/3109859.3109920","url":null,"abstract":"Search ranking is a fundamental problem of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked and the parties affected by ranking, each search ranking problem is somewhat specific. Correspondingly, search ranking at Airbnb is quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this talk, I will discuss challenges we have encountered and Machine Learning solutions we have developed for listing ranking at Airbnb. Specifically, the listing ranking problem boils down to prioritizing listings that are appealing to the guest but at the same time demoting listings that would likely reject the guest, which is not easily solvable using basic matrix completion or a straightforward linear model. I will shed the light on how we jointly optimize the two objectives by leveraging listing quality, location relevance, reviews, host response time as well as guest and host preferences and past booking history. Finally, we will talk about our recent work on using neural network models to train listing and query embeddings for purposes of enhancing search personalization, broad search and type-ahead suggestions, which are core concepts in any modern search.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121036250","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":"Recommendation Applications and Systems at Electronic Arts","authors":"Meng Wu, J. Kolen, Navid Aghdaie, Kazi A. Zaman","doi":"10.1145/3109859.3109928","DOIUrl":"https://doi.org/10.1145/3109859.3109928","url":null,"abstract":"The digital game industry has recently adopted recommendation systems to provide suitable game and content choices to players. Recommendations in digital games have several unique applications and challenges compared to other well known recommendation system such as those for movies and books. Designers must adopt different architectures and algorithms to overcome these challenges. In this talk, we describe the game recommendation system at Electronic Arts. It leverages heterogeneous player data across many games to provide intelligent recommendations. We discuss three example applications: recommending games for purchase, suitable game map, and game difficulty. Like the movie and book recommendation problem, one application is to recommend the next favorite games for a player. Digital games fall into a wide range of genres such as first player shooting (FPS), sports, and role-playing games (RPG). Games within the same genre however tend to be unique and creative. While the recommendation item space is smaller, the recommendation system should also manage different types of contents such as games and extra downloadable contents to play, editorial videos and tutorials to watch. The second application provides the game mode and map recommendations within a game to improve player experience. Many online digital games, especially FPS and sports games, contain different maps and game modes to provide diverse gameplay experience. Different maps and game modes often require different skill levels, strategies, or cooperation from players, and the maps and game modes are often played repeatedly. Therefore, recommending the most suitable map and game mode is important from smooth onboarding experience to retain players who are likely to churn. In the map and game mode recommendation application, the algorithms need to evaluate both the short-term actions as well as long term effects of playing different maps and game modes to optimize player's engagement. In addition, we also use the same recommendation system to adjust in-game configurations such as difficulty. Players have a wide variety of experiences, skills, learning rates, and playing styles, and will react differently to the same difficulty setting. Second, even for an individual player, one's difficulty preference may also change over time. For example, in a level progression game, a player who loses the first several attempts to one level might feel much less frustrated compared to losing after tens of unsuccessful trials. The difficulty recommendation provides suggestions and adjustments on game configuration based on the player's previous gameplay experience to maximize the engagement. For online multiplayer games, recommending partners and opponents in matchmaking is also an effective way to improve player experience. We developed one flexible recommendation system to satisfy the need of different applications and that executes data-driven algorithms such collaborative filtering and multi-armed","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122738958","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":"Building Recommender Systems for Fashion: Industry Talk Abstract","authors":"Nick Landia","doi":"10.1145/3109859.3109929","DOIUrl":"https://doi.org/10.1145/3109859.3109929","url":null,"abstract":"There has been a lot of recent interest in building recommender systems for fashion, with increased attention and investment from the retail industry. For academia, the fashion domain presents new challenges and opportunities that have not been explored before. Dressipi is a personalisation and style advice engine for women's fashion. We work with some of the biggest retailers in the UK who have integrated our service into their site, and are currently expanding to the US and Australia. Since our launch in 2011 we have been helping millions of users find the clothes that they will love, buy and keep. In this talk I will discuss the unique characteristics of the fashion domain and some of the most interesting challenges they pose for recommender systems. Fashion is inherently social and public: we dress not only for ourselves but also for the appropriateness of the environment we are in. When a user buys clothes it is not only important that they like the items themselves, but also that they feel confident and comfortable in the situation they are in. Fashion recommendations must satisfy two sometimes competing objectives: identifying the user's personal preference from their past behaviour and giving advice on what changes to their style would make them look better. Unlike other domains, recommendations should not be purely based on the user's personal taste and past activity. They must also take public perception into account by being aware of fashion rules, outfit guidelines and current trends. Many companies providing recommendations in this space have realised that the user-item interaction data alone can only get you so far. We have started gathering additional personal information about the users in questionnaires, if they wish to provide it. Examples of this include body shape, age, favourite colours, lifestyle etc. These additional data points allow for some exciting applications such as giving style advice and generating high quality recommendation reasons that are useful to the user. For example: `A bodycon dress is a figure flaunting style for your slender frame'. The main challenges addressed in this talk are: • Users are looking for guidance and validation that their fashion choices present the best version of themselves. • There are objective fashion do's and dont's that professional stylists know about but users might not. • Trends and popular culture events influence user preference and public perception quickly and sometimes drastically. • Good recommendation reasons are extremely important, especially when trying to give advice and recommend items outside of the user's comfort zone. • Outfits: a big factor in the decision to buy an item is the user's existing wardrobe. It is important for them to know whether they can wear the new item together with garments they already own to create good outfits.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134474322","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":"KidRec: Children & Recommender Systems: Workshop Co-located with ACM Conference on Recommender Systems (RecSys 2017)","authors":"J. A. Fails, M. S. Pera, F. Garzotto, M. Gelsomini","doi":"10.1145/3109859.3109956","DOIUrl":"https://doi.org/10.1145/3109859.3109956","url":null,"abstract":"The 1st Workshop on Children and Recommender Systems (KidRec) is taking place in Como, Italy August 27th, 2017 in conjunction with the ACM RecSys 2017 conference. The goals of the workshop are threefold: (1) discuss and identify issues related to recommender systems used by children including specific challenges and limitations, (2) discuss possible solutions to the identified challenges and plan for future research, and (3) build a community to directly work on these important issues.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134080458","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":"Secure Multi-Party Protocols for Item-Based Collaborative Filtering","authors":"E. Shmueli, Tamir Tassa","doi":"10.1145/3109859.3109881","DOIUrl":"https://doi.org/10.1145/3109859.3109881","url":null,"abstract":"Recommender systems have become extremely common in recent years, and are utilized in a variety of domains such as movies, music, news, products, restaurants, etc. While a typical recommender system bases its recommendations solely on users' preference data collected by the system itself, the quality of recommendations can significantly be improved if several recommender systems (or vendors) share their data. However, such data sharing poses significant privacy and security challenges, both to the vendors and the users. In this paper we propose secure protocols for distributed item-based Collaborative Filtering. Our protocols allow to compute both the predicted ratings of items and their predicted rankings, without compromising privacy nor predictions' accuracy. Unlike previous solutions in which the secure protocols are executed solely by the vendors, our protocols assume the existence of a mediator that performs intermediate computations on encrypted data supplied by the vendors. Such a mediated setting is advantageous over the non-mediated one since it enables each vendor to communicate solely with the mediator. This yields reduced communication costs and it allows each vendor to issue recommendations to its clients without being dependent on the availability and willingness of the other vendors to collaborate.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130330190","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":"MPR: Multi-Objective Pairwise Ranking","authors":"Rasaq Otunba, R. A. Rufai, Jessica Lin","doi":"10.1145/3109859.3109903","DOIUrl":"https://doi.org/10.1145/3109859.3109903","url":null,"abstract":"The recommendation challenge can be posed as the problem of predicting either item ratings or item rankings. The latter approach has proven more effective. Pairwise learning-to-rank techniques have been relatively successful. Hence, they are popularly used for learning recommender model parameters such as those in collaborative filtering (CF) models. The model parameters are learned by optimizing close smooth approximations of the non-smooth information retrieval (IR) metrics such as Mean Area Under ROC curve (AUC). Targeted campaigns are an alternative to item recommendations for increasing conversion. The user ranking task is referred to as audience retrieval. It is used in targeted campaigns to rank push campaign recipients based on their potential to convert. In this work, we consider the task of efficiently learning a ranking model that provides item recommendations and user rankings simultaneously. We adopt pairwise learning for this task. We refer to our approach as multi-objective pairwise ranking (MPR). We describe our approach and use experiments to evaluate its performance.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"6 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132547118","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":"3D Convolutional Networks for Session-based Recommendation with Content Features","authors":"T. Tuan, Tu Minh Phuong","doi":"10.1145/3109859.3109900","DOIUrl":"https://doi.org/10.1145/3109859.3109900","url":null,"abstract":"In many real-life recommendation settings, user profiles and past activities are not available. The recommender system should make predictions based on session data, e.g. session clicks and descriptions of clicked items. Conventional recommendation approaches, which rely on past user-item interaction data, cannot deliver accurate results in these situations. In this paper, we describe a method that combines session clicks and content features such as item descriptions and item categories to generate recommendations. To model these data, which are usually of different types and nature, we use 3-dimensional convolutional neural networks with character-level encoding of all input data. While 3D architectures provide a natural way to capture spatio-temporal patterns, character-level networks allow modeling different data types using their raw textual representation, thus reducing feature engineering effort. We applied the proposed method to predict add-to-cart events in e-commerce websites, which is more difficult then predicting next clicks. On two real datasets, our method outperformed several baselines and a state-of-the-art method based on recurrent neural networks.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"161 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120978775","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":"Product Recommendations Enhanced with Reviews","authors":"M. Chelliah, S. Sarkar","doi":"10.1145/3109859.3109936","DOIUrl":"https://doi.org/10.1145/3109859.3109936","url":null,"abstract":"User-written product reviews contain rich information about user preferences for product features and provide helpful explanations that are often used by shoppers to make their purchase decisions. E-commerce recommender systems can benefit enormously by also exploiting experiences of multiple customers captured in product reviews. In this tutorial, we present a range of techniques that allow recommender systems in e-commerce websites to take full advantage of reviews. This includes text mining methods for feature-specific sentiment analysis of products, topic models and distributed representations that bridge the vocabulary gap between user reviews and product descriptions. We present recommender algorithms that use review information to address the cold-start problem and generate recommendations with explanations. We discuss examples and experiences from an online marketplace (i.e., Flipkart).","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"455 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115389282","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":"CheckInShop.eu: A Sensor-based Recommender System for micro-location Marketing","authors":"P. Symeonidis, Stergios Chairistanidis","doi":"10.1145/3109859.3109977","DOIUrl":"https://doi.org/10.1145/3109859.3109977","url":null,"abstract":"CheckInShop is an app that employs sensors to capture the user preferences in physical stores and provide either micro-location marketing or product recommendations. By utilizing iBeacon technology and with the exploitation of a mobile app, we keep track of customer's preferences in physical stores. Then, based on the time that a product is viewed by a customer and his micro-location inside the store, we send to him either product offers or recommendations of similar products to the ones he is looking at. These recommendations are accurate because they are provided at the right time and in the right place. A video that demonstrates our system can be found in the following link: https://www.youtube.com/watch?v=Z99IMCHowAA","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115733313","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":"Educational Question Routing in Online Student Communities","authors":"Jakub Macina, Ivan Srba, J. Williams, M. Bieliková","doi":"10.1145/3109859.3109886","DOIUrl":"https://doi.org/10.1145/3109859.3109886","url":null,"abstract":"Students' performance in Massive Open Online Courses (MOOCs) is enhanced by high quality discussion forums or recently emerging educational Community Question Answering (CQA) systems. Nevertheless, only a small number of students answer questions asked by their peers. This results in instructor overload, and many unanswered questions. To increase students' participation, we present an approach for recommendation of new questions to students who are likely to provide answers. Existing approaches to such question routing proposed for non-educational CQA systems tend to rely on a few experts, what is not applicable in educational domain where it is important to involve all kinds of students. In tackling this novel educational question routing problem, our method (1) goes beyond previous question-answering data as it incorporates additional non-QA data from the course (to improve prediction accuracy and to involve more of the student community) and (2) applies constraints on users' workload (to prevent user overloading). We use an ensemble classifier for predicting students' willingness to answer a question, as well as students' expertise for answering. We conducted an online evaluation of the proposed method using an A/B experiment in our CQA system deployed in edX MOOC. The proposed method outperformed a baseline method (non-educational question routing enhanced with workload restriction) by improving recommendation accuracy, keeping more community members active, and increasing an average number of their contributions.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123715559","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}