E-commerce merchants need to optimize their recommendations and sort listings on multi-dimensional requirements beyond product attributes to include supplier considerations, long-term customer experience and the value of the sale to achieve long term success. Product recommendations for optimizing customer conversion can be modeled effectively with predictive analytic methodologies. However, supplier and customer experience elements are not easily modeled in the same manner. This paper outlines an algorithmic approach for these considerations from Expedia's experiences.
{"title":"Considering Supplier Relations and Monetization in Designing Recommendation Systems","authors":"Jan Krasnodebski, J. Dines","doi":"10.1145/2959100.2959124","DOIUrl":"https://doi.org/10.1145/2959100.2959124","url":null,"abstract":"E-commerce merchants need to optimize their recommendations and sort listings on multi-dimensional requirements beyond product attributes to include supplier considerations, long-term customer experience and the value of the sale to achieve long term success. Product recommendations for optimizing customer conversion can be modeled effectively with predictive analytic methodologies. However, supplier and customer experience elements are not easily modeled in the same manner. This paper outlines an algorithmic approach for these considerations from Expedia's experiences.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122949240","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}
This paper summarizes RecProfile '16, the first workshop on profiling user preferences for dynamic, online, and real-time recommendations, held in conjunction with RecSys '16, the 10th ACM conference on recommender systems. We describe the main themes arising in the workshop's papers.
{"title":"RecProfile '16: Workshop on Profiling User Preferences for Dynamic, Online, and Real-Time recommendations","authors":"Rani Nelken","doi":"10.1145/2959100.2959204","DOIUrl":"https://doi.org/10.1145/2959100.2959204","url":null,"abstract":"This paper summarizes RecProfile '16, the first workshop on profiling user preferences for dynamic, online, and real-time recommendations, held in conjunction with RecSys '16, the 10th ACM conference on recommender systems. We describe the main themes arising in the workshop's papers.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124288368","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}
Many recommendation techniques rely on the knowledge of preferences data in the form of ratings for items. In this paper, we focus on pairwise preferences as an alternative way for acquiring user preferences and building recommendations. In our scenario, users provide pairwise preference scores for a set of item pairs, indicating how much one item in each pair is preferred to the other. We propose a matrix factorization (MF) and a nearest neighbor (NN) prediction techniques for pairwise preference scores. Our MF solution maps users and items pairs to a joint latent features vector space, while the proposed NN algorithm leverages specific user-to-user similarity functions well suited for comparing users preferences of that type. We compare our approaches to state of the art solutions and show that our solutions produce more accurate pairwise preferences and ranking predictions.
{"title":"Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques","authors":"Saikishore Kalloori, F. Ricci, M. Tkalcic","doi":"10.1145/2959100.2959142","DOIUrl":"https://doi.org/10.1145/2959100.2959142","url":null,"abstract":"Many recommendation techniques rely on the knowledge of preferences data in the form of ratings for items. In this paper, we focus on pairwise preferences as an alternative way for acquiring user preferences and building recommendations. In our scenario, users provide pairwise preference scores for a set of item pairs, indicating how much one item in each pair is preferred to the other. We propose a matrix factorization (MF) and a nearest neighbor (NN) prediction techniques for pairwise preference scores. Our MF solution maps users and items pairs to a joint latent features vector space, while the proposed NN algorithm leverages specific user-to-user similarity functions well suited for comparing users preferences of that type. We compare our approaches to state of the art solutions and show that our solutions produce more accurate pairwise preferences and ranking predictions.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124854486","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}
In this paper, we summarize RecTour 2016 -- a workshop on recommenders in tourism co-located with RecSys 2016. There was a great variety of submissions, i.e., research papers, demo papers and position papers, addressing fundamental challenges of recommender systems in the tourism domain. The main topics included group recommendations, context-aware recommenders, choice-based recommenders and event recommendations.
{"title":"RecTour 2016: Workshop on Recommenders in Tourism","authors":"D. Fesenmaier, T. Kuflik, J. Neidhardt","doi":"10.1145/2959100.2959205","DOIUrl":"https://doi.org/10.1145/2959100.2959205","url":null,"abstract":"In this paper, we summarize RecTour 2016 -- a workshop on recommenders in tourism co-located with RecSys 2016. There was a great variety of submissions, i.e., research papers, demo papers and position papers, addressing fundamental challenges of recommender systems in the tourism domain. The main topics included group recommendations, context-aware recommenders, choice-based recommenders and event recommendations.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123563828","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}
We study the effect of the volume of consumer reviews on the purchase likelihood (conversion rate) of users browsing a product page. We propose using the exponential learning curve model to study how conversion rates change with the number of reviews. We call the difference in conversion rate between having no reviews and an infinite number the value of reviews. We find that, on average, the conversion rate of a product can increase by as much as 270% as it accumulates reviews, amongst the users that choose to display them. We also find diminishing marginal value as a product accumulates reviews, with the first five reviews driving the bulk of the aforementioned increase. To address the problem of simultaneity of increase of reviews and conversion rate, we use customer sessions in which reviews were not displayed as a control for trends that would have happened regardless of the increase in the review volume. Using our framework, we further find that high priced items have a higher value for reviews than lower priced items. High priced items can see their conversion rate increase by as much as 380% as they accumulate reviews compared to 190% for low priced items.We infer that the existence of reviews provides valuable signals to the customers, increasing their propensity to purchase. We also infer that users usually don't pay attention to the entire set of reviews, especially if there are a lot of them, but instead they focus on the first few available. Our approach can be extended and applied in a variety of settings to gain further insights.
{"title":"The Value of Online Customer Reviews","authors":"Georgios Askalidis, E. Malthouse","doi":"10.1145/2959100.2959181","DOIUrl":"https://doi.org/10.1145/2959100.2959181","url":null,"abstract":"We study the effect of the volume of consumer reviews on the purchase likelihood (conversion rate) of users browsing a product page. We propose using the exponential learning curve model to study how conversion rates change with the number of reviews. We call the difference in conversion rate between having no reviews and an infinite number the value of reviews. We find that, on average, the conversion rate of a product can increase by as much as 270% as it accumulates reviews, amongst the users that choose to display them. We also find diminishing marginal value as a product accumulates reviews, with the first five reviews driving the bulk of the aforementioned increase. To address the problem of simultaneity of increase of reviews and conversion rate, we use customer sessions in which reviews were not displayed as a control for trends that would have happened regardless of the increase in the review volume. Using our framework, we further find that high priced items have a higher value for reviews than lower priced items. High priced items can see their conversion rate increase by as much as 380% as they accumulate reviews compared to 190% for low priced items.We infer that the existence of reviews provides valuable signals to the customers, increasing their propensity to purchase. We also infer that users usually don't pay attention to the entire set of reviews, especially if there are a lot of them, but instead they focus on the first few available. Our approach can be extended and applied in a variety of settings to gain further insights.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129807330","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}
Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek to learn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be effective for large preference datasets is Restricted Boltzmann Machine (RBM). While homophily, or the tendency of friends to share their preferences at some level, is an established notion in sociology, thus far it has not yet been clearly demonstrated on RBM-based preference models. The question lies in how to appropriately incorporate social network into the architecture of RBM-based models for learning representations of preferences. In this paper, we propose two potential architectures: one that models social network among users as additional observations, and another that incorporates social network into the sharing of hidden units among related users. We study the efficacies of these proposed architectures on publicly available, real-life preference datasets with social networks, yielding useful insights.
{"title":"Representation Learning for Homophilic Preferences","authors":"Trong-The Nguyen, Hady W. Lauw","doi":"10.1145/2959100.2959157","DOIUrl":"https://doi.org/10.1145/2959100.2959157","url":null,"abstract":"Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek to learn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be effective for large preference datasets is Restricted Boltzmann Machine (RBM). While homophily, or the tendency of friends to share their preferences at some level, is an established notion in sociology, thus far it has not yet been clearly demonstrated on RBM-based preference models. The question lies in how to appropriately incorporate social network into the architecture of RBM-based models for learning representations of preferences. In this paper, we propose two potential architectures: one that models social network among users as additional observations, and another that incorporates social network into the sharing of hidden units among related users. We study the efficacies of these proposed architectures on publicly available, real-life preference datasets with social networks, yielding useful insights.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128937195","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}
We will demonstrate a conversational products recommendation agent. This system shows how we combine research in personalized recommendation systems with research in dialogue systems to build a virtual sales agent. Based on new deep learning technologies we developed, the virtual agent is capable of learning how to interact with users, how to answer user questions, what is the next question to ask, and what to recommend when chatting with a human user. Normally a descent conversational agent for a particular domain requires tens of thousands of hand labeled conversational data or hand written rules. This is a major barrier when launching a conversation agent for a new domain. We will explore and demonstrate the effectiveness of the learning solution even when there is no hand written rules or hand labeled training data.
{"title":"Conversational Recommendation System with Unsupervised Learning","authors":"Yueming Sun, Yi Zhang, Yunfei Chen, Roger Jin","doi":"10.1145/2959100.2959114","DOIUrl":"https://doi.org/10.1145/2959100.2959114","url":null,"abstract":"We will demonstrate a conversational products recommendation agent. This system shows how we combine research in personalized recommendation systems with research in dialogue systems to build a virtual sales agent. Based on new deep learning technologies we developed, the virtual agent is capable of learning how to interact with users, how to answer user questions, what is the next question to ask, and what to recommend when chatting with a human user. Normally a descent conversational agent for a particular domain requires tens of thousands of hand labeled conversational data or hand written rules. This is a major barrier when launching a conversation agent for a new domain. We will explore and demonstrate the effectiveness of the learning solution even when there is no hand written rules or hand labeled training data.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125287352","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}
Recent research on Recommender Systems, specifically Collaborative Filtering, has focussed on Matrix Factorization (MF) methods, which have been shown to provide good solutions to the cold start problem. However, typically the same settings are used for Matrix factorization regardless of the density of the matrix. In our experiments, we found that for MF, Root Mean Square Error (RMSE) for recommendations increases (i.e. performance drops) for sparse matrices. We propose a Two Stage MF approach so MF is run twice over the whole matrix; the first stage uses MF to generate a small percentage of pseudotransactions that are added to the original matrix to increase its density, and the second stage re-runs MF over this denser matrix to predict the user-item transactions in the testing set. We show using data from Movielens that such methods can improve on the performance of MF for sparse martrices.
{"title":"Generating Pseudotransactions for Improving Sparse Matrix Factorization","authors":"A. Wibowo","doi":"10.1145/2959100.2959107","DOIUrl":"https://doi.org/10.1145/2959100.2959107","url":null,"abstract":"Recent research on Recommender Systems, specifically Collaborative Filtering, has focussed on Matrix Factorization (MF) methods, which have been shown to provide good solutions to the cold start problem. However, typically the same settings are used for Matrix factorization regardless of the density of the matrix. In our experiments, we found that for MF, Root Mean Square Error (RMSE) for recommendations increases (i.e. performance drops) for sparse matrices. We propose a Two Stage MF approach so MF is run twice over the whole matrix; the first stage uses MF to generate a small percentage of pseudotransactions that are added to the original matrix to increase its density, and the second stage re-runs MF over this denser matrix to predict the user-item transactions in the testing set. We show using data from Movielens that such methods can improve on the performance of MF for sparse martrices.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129941735","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}
Despite their high priority, healthy nutrition, physical activity and other preventive health factors are rarely adopted over a long term. Traditional nutrition support systems lack of practical everyday knowledge, social support and motivation as well the consideration of the personal context. We address these impediments with a holistic decision support system that empowers people to change their lifestyle successfully. We aim at analyzing previous approaches from various disciplines and integrating them into one concept for nutrition support giving personalized context aware recommendations to each user while presenting and teaching practical information about healthy nutrition. Additionally, we consider the ease of usage of such an application by automating necessary burdens and motivating participants with social incentives. The decision support system is tested in a 6-month intervention study using regression analysis on usage patterns and matrix factorial designs for interacting features. The social and interactive components are observed in a 1-year field study, utilizing a realistic environment.
{"title":"Personalized Support for Healthy Nutrition Decisions","authors":"Hanna Schäfer","doi":"10.1145/2959100.2959105","DOIUrl":"https://doi.org/10.1145/2959100.2959105","url":null,"abstract":"Despite their high priority, healthy nutrition, physical activity and other preventive health factors are rarely adopted over a long term. Traditional nutrition support systems lack of practical everyday knowledge, social support and motivation as well the consideration of the personal context. We address these impediments with a holistic decision support system that empowers people to change their lifestyle successfully. We aim at analyzing previous approaches from various disciplines and integrating them into one concept for nutrition support giving personalized context aware recommendations to each user while presenting and teaching practical information about healthy nutrition. Additionally, we consider the ease of usage of such an application by automating necessary burdens and motivating participants with social incentives. The decision support system is tested in a 6-month intervention study using regression analysis on usage patterns and matrix factorial designs for interacting features. The social and interactive components are observed in a 1-year field study, utilizing a realistic environment.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129980785","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}
This proposed thesis work explores two research areas in the domain of Recommender Systems [RS] , algorithms and their real world applications. First is related to identification of Gray Sheep [GS] users and Influential Users [IU] in any system using different personality models and also creating psychographic profile of such users. The second part of this work is an empirical study to find out the determinant of RS adoption in Start-ups context of developing nations, by using diffusion of innovation (DOI) theory and Technology-Organization-Environment (TOE) frameworks.
{"title":"Gray Sheep, Influential Users, User Modeling and Recommender System Adoption by Startups","authors":"Abhishek Srivastava","doi":"10.1145/2959100.2959103","DOIUrl":"https://doi.org/10.1145/2959100.2959103","url":null,"abstract":"This proposed thesis work explores two research areas in the domain of Recommender Systems [RS] , algorithms and their real world applications. First is related to identification of Gray Sheep [GS] users and Influential Users [IU] in any system using different personality models and also creating psychographic profile of such users. The second part of this work is an empirical study to find out the determinant of RS adoption in Start-ups context of developing nations, by using diffusion of innovation (DOI) theory and Technology-Organization-Environment (TOE) frameworks.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122034111","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}
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