Proceedings of the 9th ACM Conference on Recommender Systems最新文献

{"title":"Replicable Evaluation of Recommender Systems","authors":"A. Said, Alejandro Bellogín","doi":"10.1145/2792838.2792841","DOIUrl":"https://doi.org/10.1145/2792838.2792841","url":null,"abstract":"Recommender systems research is by and large based on comparisons of recommendation algorithms' predictive accuracies: the better the evaluation metrics (higher accuracy scores or lower predictive errors), the better the recommendation algorithm. Comparing the evaluation results of two recommendation approaches is however a difficult process as there are very many factors to be considered in the implementation of an algorithm, its evaluation, and how datasets are processed and prepared. This tutorial shows how to present evaluation results in a clear and concise manner, while ensuring that the results are comparable, replicable and unbiased. These insights are not limited to recommender systems research alone, but are also valid for experiments with other types of personalized interactions and contextual information access.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124675582","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":"Exploiting Geo-Spatial Preference for Personalized Expert Recommendation","authors":"Haokai Lu, James Caverlee","doi":"10.1145/2792838.2800189","DOIUrl":"https://doi.org/10.1145/2792838.2800189","url":null,"abstract":"Experts are important for providing reliable and authoritative information and opinion, as well as for improving online reviews and services. While considerable previous research has focused on finding topical experts with broad appeal -- e.g., top Java developers, best lawyers in Texas -- we tackle the problem of personalized expert recommendation, to identify experts who have special personal appeal and importance to users. One of the key insights motivating our approach is to leverage the geo-spatial preferences of users and the variation of these preferences across different regions, topics, and social communities. Through a fine-grained GPS-tagged social media trace, we characterize these geo-spatial preferences for personalized experts, and integrate these preferences into a matrix factorization-based personalized expert recommender. Through extensive experiments, we find that the proposed approach can improve the quality of recommendation by 24% in precision compared to several baselines. We also find that users' geo-spatial preference of expertise and their underlying social communities can ameliorate the cold start problem by more than 20% in precision and recall.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114802270","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":"TouRS'15: Workshop on Tourism Recommender Systems","authors":"Antonio Moreno, L. Sebastiá, P. Vansteenwegen","doi":"10.1145/2792838.2798713","DOIUrl":"https://doi.org/10.1145/2792838.2798713","url":null,"abstract":"Tourism has been one of the most prominents fields of application of recommender systems in the last ten years.This summary gives an overview of the latest advances in the area, which have been presented in the RecSys 2015 workshop on Tourism Recommender Systems.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"314 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131964615","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":"Adaptation and Evaluation of Recommendations for Short-term Shopping Goals","authors":"D. Jannach, Lukas Lerche, Michael Jugovac","doi":"10.1145/2792838.2800176","DOIUrl":"https://doi.org/10.1145/2792838.2800176","url":null,"abstract":"An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on long-term user models that are pre-trained on historical data can therefore be insufficient for a suitable next-basket recommendation. Simple \"real-time\" recommendation approaches based, e.g., on unpersonalized co-occurrence patterns, on the other hand do not fully exploit the available information about the user's long-term preference profile. In this work, we aim to explore and quantify the effectiveness of using and combining long-term models and short-term adaptation strategies. We conducted an empirical evaluation based on a novel evaluation design and two real-world datasets. The results indicate that maintaining short-term content-based and recency-based profiles of the visitors can lead to significant accuracy increases. At the same time, the experiments show that the choice of the algorithm for learning the long-term preferences is particularly important at the beginning of new shopping sessions.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134374687","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":"Scalable Recommender Systems: Where Machine Learning Meets Search","authors":"Si Ying Diana Hu, Joaquin Delgado","doi":"10.1145/2792838.2792842","DOIUrl":"https://doi.org/10.1145/2792838.2792842","url":null,"abstract":"This tutorial provides an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction-based systems. In particular, we will review ML-Scoring, an open source framework, created by the authors that tightly integrates machine-learning models into Elasticsearch, a popular search engine that is distributed, scalable, highly available with real-time search and analytic functionalities. The fundamentals and basic methods in information retrieval and machine learning will be explained. Accompanying the theory, practical examples will illustrate their applications with a series of hands-on exercises. These will demonstrate how to load a dataset into Elasticsearch, how to train a model in an external software framework such as Spark, Weka, or R, and finally how to load the trained models as a ML-Scoring plugins created for Elasticsearch.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132912141","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":"E-commerce Recommendation with Personalized Promotion","authors":"Qi Zhao, Yi Zhang, D. Friedman, Fangfang Tan","doi":"10.1145/2792838.2800178","DOIUrl":"https://doi.org/10.1145/2792838.2800178","url":null,"abstract":"Most existing e-commerce recommender systems aim to recommend the right products to a consumer, assuming the properties of each product are fixed. However, some properties, including price discount, can be personalized to respond to each consumer's preference. This paper studies how to automatically set the price discount when recommending a product, in light of the fact that the price will often alter a consumer's purchase decision. The key to optimizing the discount is to predict consumer's willingness-to-pay (WTP), namely, the highest price a consumer is willing to pay for a product. Purchase data used by traditional e-commerce recommender systems provide points below or above the decision boundary. In this paper we collected training data to better predict the decision boundary. We implement a new e-commerce mechanism adapted from laboratory lottery and auction experiments that elicit a rational customer's exact WTP for a small subset of products, and use a machine learning algorithm to predict the customer's WTP for other products. The mechanism is implemented on our own e-commerce website that leverages Amazon's data and subjects recruited via Mechanical Turk. The experimental results suggest that this approach can help predict WTP, and boost consumer satisfaction as well as seller profit.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128735354","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":"We Know Where You Should Work Next Summer: Job Recommendations","authors":"F. Abel","doi":"10.1145/2792838.2799496","DOIUrl":"https://doi.org/10.1145/2792838.2799496","url":null,"abstract":"Business-oriented social networks like LinkedIn or XING support people in discovering career opportunities. In this talk, we will focus on the problem of recommending job offers to Millions of XING users. We will discuss challenges of building a job recommendation system that has to satisfy the demands of both job seekers who have certain wishes concerning their next career step and recruiters who aim to hire the most appropriate candidate for a job. Based on insights gained from a large-scale analysis of usage data and profile data such as curriculum vitae, we will study features of the recommendation algorithms that aim to solve the problem. Job advertisements typically describe the job role that the candidate will need to fill, required skills, the expected educational background that candidates should have and the company and environment in which candidates will be working. Users of professional social networks curate their profile and curriculum vitae in which they describe their skills, interests and previous career steps. Recommending jobs to users is however a non-trivial task for which pure content-based features that would just match the aforementioned properties are not sufficient. For example, we often observe that there is a gap between what people specify in their profiles and what they are actually interested in. Moreover, profile and CV typically describe the past and current situation of a user but do not reflect enough the actual demands that users have with respect to their next career step. Therefore, it is crucial to also analyze the behavior of the users and exploit interaction data such as search queries, clicks on jobs, bookmarks, clicks that similar users performed, etc. Our job recommendation system exploits various features in order to estimate whether a job posting is relevant for a user or not. Some of these features rather reflect social aspects (e.g. does the user have contacts that are living in the city in which the job is offered?) while others capture to what extent the user fulfills the requirements of the role that is described in the job advertisement (e.g. similarity of user's skills and required skills). To better understand appropriate next career steps, we mine the CVs of the users and learn association rules that describe the typical career paths. This information is also made publicly available via FutureMe - a tool that allows people to explore possible career opportunities and identify professions that may be interesting for them to work in. One of the challenges when developing the job recommendation system is to collect explicit feedback and thus understanding (i) whether a recommended job was relevant for a user and (ii) whether the user was a good candidate for the job. We thus started to stronger involve users in providing feedback and build a feedback cycle that allows the recommender system to automatically adapt to the feedback that the crowd of users is providing. By displaying explanations a","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133915780","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":"Asymmetric Recommendations: The Interacting Effects of Social Ratings? Direction and Strength on Users' Ratings","authors":"O. Nov, Ofer Arazy","doi":"10.1145/2792838.2799667","DOIUrl":"https://doi.org/10.1145/2792838.2799667","url":null,"abstract":"In social recommendation systems, users often publicly rate objects such as photos, news articles or consumer products. When they appear in aggregate, these ratings carry social signals such as the direction and strength of the raters' average opinion about the product. Using a controlled experiment we manipulated two central social signals -- the direction and strength of social ratings of five popular consumer products -- and examined their interacting effects on users' ratings. The results show an asymmetric user behavior, where the direction of perceived social rating has a negative effect on users' ratings if the direction of perceived social rating is negative, but no effect if the direction is positive. The strength of perceived social ratings did not have a significant effect on users' ratings. The findings highlight the potential for cascading adverse effects of small number of negative user ratings on subsequent users' opinions.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133922408","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":"Nuke 'Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders","authors":"C. E. Seminario, David C. Wilson","doi":"10.1145/2792838.2799666","DOIUrl":"https://doi.org/10.1145/2792838.2799666","url":null,"abstract":"Recommender Systems (RSs) can be vulnerable to manipulation by malicious users who successfully bias recommendations for their own benefit or pleasure. These are known as attacks on RSs and are typically used to either promote (\"push\") or disparage (\"nuke\") targeted items contained within the recommender's user-item dataset. Our recent work with the Power User Attack (PUA) model, determined that attackers disguised as influential power users can mount successful (from the attacker's viewpoint) push attacks against user-based, item-based, and SVD-based recommenders. However, the success of push attack vectors may not be symmetric for nuke attacks, which target the opposite effect --- reducing the likelihood that target items appear in users' top-N lists. The asymmetry between push and nuke attacks is highlighted when evaluating these attacks using traditional robustness metrics such as Rank and Prediction Shift. This paper examines the PUA attack model in the context of nuke attacks, in order to investigate the differences between push and nuke attack orientations, as well as how they are evaluated. In this work we show that the PUA is able to mount successful nuke attacks against commonly-used recommender algorithms highlighting the \"nuke vs. push\" asymmetry in the results.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134640201","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":"Are Real-World Place Recommender Algorithms Useful in Virtual World Environments?","authors":"L. Marinho, C. Trattner, Denis Parra","doi":"10.1145/2792838.2799674","DOIUrl":"https://doi.org/10.1145/2792838.2799674","url":null,"abstract":"Large scale virtual worlds such as massive multiplayer online games or 3D worlds gained tremendous popularity over the past few years. With the large and ever increasing amount of content available, virtual world users face the information overload problem. To tackle this issue, game-designers usually deploy recommendation services with the aim of making the virtual world a more joyful environment to be connected at. In this context, we present in this paper the results of a project that aims at understanding the mobility patterns of virtual world users in order to derive place recommenders for helping them to explore content more efficiently. Our study focus on the virtual world SecondLife, one of the largest and most prominent in recent years. Since SecondLife is comparable to real-world Location-based Social Networks (LBSNs), i.e., users can both check-in and share visited virtual places, a natural approach is to assume that place recommenders that are known to work well on real-world LBSNs will also work well on SecondLife. We have put this assumption to the test and found out that (i) while collaborative filtering algorithms have compatible performances in both environments, (ii) existing place recommenders based on geographic metadata are not useful in SecondLife.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114335576","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}