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

{"title":"How can they know that? A study of factors affecting the creepiness of recommendations","authors":"Helma Torkamaan, Catalin-Mihai Barbu, J. Ziegler","doi":"10.1145/3298689.3346982","DOIUrl":"https://doi.org/10.1145/3298689.3346982","url":null,"abstract":"Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users' feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users' expectations of---and their trust in---RS.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"63 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":"121088623","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":"The trinity of luxury fashion recommendations: data, experts and experimentation","authors":"A. Magalhães","doi":"10.1145/3298689.3346978","DOIUrl":"https://doi.org/10.1145/3298689.3346978","url":null,"abstract":"Farfetch is the leading platform for online luxury fashion shopping. We have more than 3000 brands and high-end designers with the biggest catalog of luxury products available worldwide to more than 1 million customers. The high-end luxury fashion segment where Farfetch operates in is a notably complex and intricate field. Fashion trends change very fast and can come from anywhere, at any time, thus being very hard to capture. Ultimately, people's tastes are very personal and hard to extrapolate. Users of luxury websites have understandably high expectations and demand a high-end, curated and knowledgeable experience in all aspects. To achieve this, the recommendations engine powering the Farfetch platform is being built on top of three main pillars: 1) data, 2) expert knowledge, and 3) experimentation. Data is obviously the core of any automated recommender system. Like many e-commerce platforms, we collect and leverage various implicit interactions by tracking our users' journeys on Farfetch.com and apps, as well as the explicit preferences they often set - such as their favourite designers. From implicit feedback data we started building the state-of-the-art recommender systems based on collaborative approaches only to realize that our catalogue would not allow for item-item collaborative recommenders, since a product's lifetime is either too short with unique pieces being bought as soon as they go live, or too long with some timeless iconic items lasting forever. Hence, we needed to implement hybrid versions of collaborative-based recommenders which emphasized the products' content data [1]. Throughout the experimentation process over these algorithms, both implicit and explicit feedback data seemed to fall short to encode the sense of fashion expected by our customers. The obvious next step was to use the internal knowledge embedded in several teams of fashion experts and stylists. Although not trivial, there are many ways we can leverage this expert knowledge into improving the fashion understanding of our recommender systems: • Our content editors create the editorial pages with the latest trends and write the products' descriptions. This data allows us to build the relationships between designers to create adjacency models and incorporate taxonomy data employing NLP approaches [5]. • Our visual merchandising experts curate crucial listing pages with products respecting business rules, fashion trends and our signature on fashion. This allows us to encode colorflow and style trends by using style transfer techniques such as computing Gram matrices from convolutional feature maps [2]. • Our stylists manually curate outfits respecting Farfetch's style identity. This allows us to build automated outfits based on siamese neural networks on top of Convolutional Neural Networks [3, 4]. In order to tie these sources of information together in a seamless manner, we follow a strict experimentation workflow, where we iterate fast, deliver in a contro","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"14 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":"122659761","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":"Bandit algorithms in recommender systems","authors":"D. Glowacka","doi":"10.1145/3298689.3346956","DOIUrl":"https://doi.org/10.1145/3298689.3346956","url":null,"abstract":"The multi-armed bandit problem models an agent that simultaneously attempts to acquire new knowledge (exploration) and optimize his decisions based on existing knowledge (exploitation). The agent attempts to balance these competing tasks in order to maximize his total value over the period of time considered. There are many practical applications of the bandit model, such as clinical trials, adaptive routing or portfolio design. Over the last decade there has been an increased interest in developing bandit algorithms for specific problems in recommender systems, such as news and ad recommendation, the cold start problem in recommendation, personalization, collaborative filtering with bandits, or combining social networks with bandits to improve product recommendation. The aim of this tutorial is to provide an overview of the various applications of bandit algorithms in recommendation.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"5 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":"131347573","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":"ACM RecSys'19 late-breaking results (posters)","authors":"M. Tkalcic, M. S. Pera","doi":"10.1145/3298689.3346970","DOIUrl":"https://doi.org/10.1145/3298689.3346970","url":null,"abstract":"As part of the main program of the 2019 ACM Recommender System Conference, the Late-Breaking Results offers a unique opportunity to share with the community the latest ideas related to recommender systems. This year, we received 42 submissions for the track, out of which 13 were accepted, resulting in a acceptance rate of 31%.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"9 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":"131703385","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":"Combining text summarization and aspect-based sentiment analysis of users' reviews to justify recommendations","authors":"C. Musto, Gaetano Rossiello, M. Degemmis, P. Lops, G. Semeraro","doi":"10.1145/3298689.3347024","DOIUrl":"https://doi.org/10.1145/3298689.3347024","url":null,"abstract":"In this paper we present a methodology to justify recommendations that relies on the information extracted from users' reviews discussing the available items. The intuition behind the approach is to conceive the justification as a summary of the most relevant and distinguishing aspects of the item, automatically obtained by analyzing its reviews. To this end, we designed a pipeline of natural language processing techniques including aspect extraction, sentiment analysis and text summarization to gather the reviews, process the relevant excerpts, and generate a unique synthesis presenting the main characteristics of the item. Such a summary is finally presented to the target user as a justification of the received recommendation. In the experimental evaluation we carried out a user study in the movie domain (N=141) and the results showed that our approach is able to make the recommendation process more transparent, engaging and trustful for the users.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"28 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":"132218256","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":"Find my next job: labor market recommendations using administrative big data","authors":"S. Frid-Nielsen","doi":"10.1145/3298689.3346992","DOIUrl":"https://doi.org/10.1145/3298689.3346992","url":null,"abstract":"Labor markets are undergoing change due to factors such as automatization and globalization, motivating the development of occupational recommender systems for jobseekers and caseworkers. This study generates occupational recommendations by utilizing a novel data set consisting of administrative records covering the entire Danish workforce. Based on actual labor market behavior in the period 2012-2015, how well can different models predict each users' next occupation in 2016? Through offline experiments, the study finds that gradient-boosted decision tree models provide the best recommendations for future occupations in terms of mean reciprocal ranking and recall. Further, gradient-boosted decision tree models offer distinct advantages in the labor market domain due to their interpretability and ability to harness additional background information on workers. However, the study raises concerns regarding trade-offs between model accuracy and ethical issues, including privacy and the social reinforcement of gender divides.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"13 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":"122247408","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":"Incorporating intent propensities in personalized next best action recommendation","authors":"Yuxi Zhang, Kexin Xie","doi":"10.1145/3298689.3346962","DOIUrl":"https://doi.org/10.1145/3298689.3346962","url":null,"abstract":"Next best action (NBA) is a technique that is widely considered as the best practice in modern personalized marketing. It takes users' unique characteristics into consideration and recommends next actions that help users progress towards business goals as quickly and smoothly as possible. Many NBA engines are built with rules handcrafted by marketers based on experience or gut feelings. It is not effective. In this proposal, we show our machine learning based approach for such a real-time recommendation engine, detail our design choices, and discuss evaluation techniques. In practice, there are several key challenges to consider. (a) It needs to be able to deal with historical feedback that is typically incomplete and skewed towards a small set of actions; (b) Actions are typically dynamic. They can be added or removed anytime due to seasonal changes or shifts in business strategies; (c) The optimization objective is typically complex. It usually consists of reaching a set of target events or moving users to more preferred stages. The engine needs to account for all these aspects. Standard classification or regression models are not suitable to use, because only bandit feedback is available and sampling bias presented in historical data can not be handled properly. Conventional multi-armed bandit model can address some of the challenges. But it lacks the ability to model multiple objectives. We present a propensity variant hybrid contextual multi-armed bandit model (PV-MAB) that can address all three challenges. PV-MAB consists of two components: an intent propensity model (I-Prop) and a hybrid contextual MAB (H-Bandit). H-Bandit can be considered as a multi-policy contextual MAB, where we model different aspects of user engagement separately and cater the policies to each unique characteristic. I-Prop leverages user intent signals to target different users toward specific goals that are most relevant to them. It acts as a policy selector, to inform H-Bandit to choose the best strategy for different users at different points in the journey. I-Prop is trained separately with features extracted from user profile affinities and past behaviors. To illustrate this design, we will focus our discussion on how to incorporate two common distinct objectives in H-bandit. The first one is to target and drive users to reach a small set of high-value goals (e.g. purchase, become superfan), called goal-oriented policy. The second is to promote progression into more advanced stages in a consumer journey (e.g. from login to complete profile). We call it stage-advancement policy. In the goal-oriented policy, we reward reaching the goals accordingly, and use classification predictor as kernel function to predict the probabilities for achieving those goals. In the stage-advancement policy, we use the progression of stages as reward. Customers can move forward in their journey, skip a few stages or go back to previous stages doing more research or re-evaluation. The ","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"28 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":"125218161","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":"Deep language-based critiquing for recommender systems","authors":"Ga Wu, Kai Luo, S. Sanner, Harold Soh","doi":"10.1145/3298689.3347009","DOIUrl":"https://doi.org/10.1145/3298689.3347009","url":null,"abstract":"Critiquing is a method for conversational recommendation that adapts recommendations in response to user preference feedback regarding item attributes. Historical critiquing methods were largely based on constraint- and utility-based methods for modifying recommendations w.r.t. these critiqued attributes. In this paper, we revisit the critiquing approach from the lens of deep learning based recommendation methods and language-based interaction. Concretely, we propose an end-to-end deep learning framework with two variants that extend the Neural Collaborative Filtering architecture with explanation and critiquing components. These architectures not only predict personalized keyphrases for a user and item but also embed language-based feedback in the latent space that in turn modulates subsequent critiqued recommendations. We evaluate the proposed framework on two recommendation datasets containing user reviews. Empirical results show that our modified NCF approach not only provides a strong baseline recommender and high-quality personalized item keyphrase suggestions, but that it also properly suppresses items predicted to have a critiqued keyphrase. In summary, this paper provides a first step to unify deep recommendation and language-based feedback in what we hope to be a rich space for future research in deep critiquing for conversational recommendation.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"12 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":"121770425","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":"Predicting user routines with masked dilated convolutions","authors":"Renzhong Wang, Dragomir Yankov, Michael R. Evans, S. Palanisamy, Siddharth Arora, Wei Wu","doi":"10.1145/3298689.3347025","DOIUrl":"https://doi.org/10.1145/3298689.3347025","url":null,"abstract":"Predicting users daily location visits - when and where they will go, and how long they will stay - is key for making effective location-based recommendations. Knowledge of an upcoming day allows the suggestion of relevant alternatives (e.g., a new coffee shop on the way to work) in advance, prior to a visit. This helps users make informed decisions and plan accordingly. People's visit routines, or just routines, can vary significantly from day to day, and visits from earlier in the day, week, or month may affect subsequent choices. Traditionally, routine prediction has been modeled with sequence methods, such as HMMs or more recently with RNN-based architectures. However, the problem with such architectures is that their predictive performance degrades when increasing the number of historical observations in the routine sequence. In this paper, we propose Masked-TCN (MTCN), a novel method based on time-dilated convolutional networks. The method implements custom dilations and masking which can process effectively long routine sequences, identifying recurring patterns at different resolution - hourly, daily, weekly, monthly. We demonstrate that MTCN achieves 8% improvement in accuracy over current state-of-the-art solutions on a large data set of visit routines.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"2 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":"127804082","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":"Designer-driven add-to-cart recommendations","authors":"Sandhya Sachidanandan, Richard Luong, Emil S. Joergensen","doi":"10.1145/3298689.3346959","DOIUrl":"https://doi.org/10.1145/3298689.3346959","url":null,"abstract":"Although real-time dynamic recommender systems have been applied successfully by e-commerce and technology companies for more than a decade, we at IKEA Group have just started our journey into this exciting field. At IKEA, customer experience is at our heart, and a key principle for any machine learning algorithm that we design to improve this experience is that it should act as an extension to the home-furnishing expertise that our co-workers have developed and fine-tuned for more than 75 years. In this talk, we discuss a particular recommendation strategy that projects the inspirational shopping experience of our blue boxes onto our digital touch points by defining a notion of style from our vast collection of inspirational content. To go beyond classical, transaction-based collaborative filtering strategies, we take as our starting point the different types of images taken of each product when launched. Our current implementation relies on the following 3 types of images: (1) white-canvas, referring to an image of a product displayed on a plain white background; (2) context-based, which shows a product in the larger context of a room, but where emphasis remains on the product itself; (3) inspirational, in which a product is shown in a purposefully atmospheric setting with focus on the entirety. By extracting the product range displayed in our tagged inspirational images, we initially construct a graph of products that embeds the mindset of our talented designers. Add-to-cart recommendations are then generated from the resulting graph based on user-behaviour data collected from our digital touch points (app, web) and transactional data from purchases made online, or in one of our IKEA stores. To implement the strategy, we have come across a few interesting (stand-alone) problems along the way; notably, we faced a severe lack of properly tagged inspirational images, and much of our furniture today does not appear in our inspirational collection. To circumvent the latter observation, we pursue a supervised learning approach that automatically identifies products that 1) complement each other with regards to function, and 2) match in terms of style. We do this by taking product metadata attributes and the full collection of product images as input. We also discuss how we use a combination of features extracted from context-based and inspirational images using a pre-trained ImageNet model [2], together with manually tagged inspirational images and transaction data from stores to create our training data. The use of both context-based and inspirational images distinguishes us from similar methodologies in the fashion industry [1, 3] and enables us to capture the notion of complementary products in a satisfying way.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"9 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":"121074123","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}