Over the years we have seen recommender systems shifting focus from optimizing short-term engagement toward improving long-term user experience on the platforms. While defining good long-term user experience is still an active research area, we focus on one specific aspect of improved long-term user experience here, which is user revisiting the platform. These long term outcomes however are much harder to optimize due to the sparsity in observing these events and low signal-to-noise ratio (weak connection) between these long-term outcomes and a single recommendation. To address these challenges, we propose to establish the association between these long-term outcomes and a set of more immediate term user behavior signals that can serve as surrogates for optimization.

Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.). These characteristics can then be used by a search engine to return results that better match the query’s product intent. Traditional methods for slot-filling require the availability of training data with ground truth slot-annotation information. However, generating representative labeled data, especially in big-data driven platforms like e-commerce is expensive and time consuming, given the volume and velocity of the data. In this paper, we present distant-supervised probabilistic generative models, that require no manual annotation. The proposed approaches leverage the readily available historical queries and their subsequent transaction logs, and also exploit co-occurrence information among the slots in order to identify intended product characteristics. We evaluate our approaches by considering both how they affect retrieval performance, as well as how well they classify the slots. In terms of retrieval, our approaches achieve better ranking performance (up to 156%) over Okapi BM25. Moreover, our approach that leverages co-occurrence information leads to better performance than the one that does not on both the retrieval and slot classification tasks.

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms and social networks, to provide suggestions that a user will most likely consume or connect; thus, improving the user experience. This motivates people in both industry and research organizations to focus on personalization or recommendation algorithms, which has resulted in a plethora of research papers. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment. These key factors include but are not limited to: business metric definition and evaluation, recommendation quality control, data and model scalability, model interpretability, model robustness and fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions for industrial recommendation systems. This workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue through which academic researchers become aware of the additional factors that may affect the adoption of an algorithm into real production systems, and how well it will perform if deployed. Industrial researchers will also benefit from sharing the practical insights, approaches, and frameworks as well.

Reinforcement Learning (RL) has been sought after to bring next-generation recommender systems to further improve user experience on recommendation platforms. While the exploration-exploitation tradeoff is the foundation of RL research, the value of exploration in (RL-based) recommender systems is less well understood. Exploration, commonly seen as a tool to reduce model uncertainty in regions of sparse user interaction/feedback, is believed to cost user experience in the short term, while the indirect benefit of better model quality arrives at a later time. We focus on another aspect of exploration, which we refer to as user exploration to help discover new user interests, and argue it can improve user experience even in the more imminent term.

E-commerce search engines can fail to retrieve results that satisfy a query's product intent because: (i) conventional retrieval approaches, such as BM25, may ignore the important terms in queries owing to their low "inverse document frequency" " (IDF), and (ii) for long queries, as is usually the case in rare queries (i.e., tail queries), they may fail to determine the relevant terms that are representative of the query's product intent. In this paper, we leverage the historical query reformulation logs of a large e-retailer (walmart.com) to develop a distant-supervision-based approach to identify the relevant terms that characterize the query's product intent. The key idea underpinning our approach is that the terms retained in the reformulation of a query are more important in describing the query's product intent than the discarded terms. Additionally, we also use the fact that the significance of a term depends on its context (other terms in the neighborhood) in the query to determine the term's importance towards the query's product intent. We show that identifying and emphasizing the terms that define the query's product intent leads to a 3% improvement in ranking and outperforms the context-unaware baselines.

Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches. Also, we show that the number of ratings that an item or a user has positively correlates with the ability of low-rank matrix-completion-based approaches to predict the ratings for the item or the user accurately. Furthermore, we use these insights to develop four matrix completion-based approaches, i.e., Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization (TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional matrix-completion-based approaches for the users and the items with few ratings in the user-item rating matrix.

Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are twofold. First, a rating provided on a set conveys some preference information about each of the set’s items, which allows us to acquire a user’s preferences for more items than the number of ratings that the user provided. Second, due to privacy concerns, users may not be willing to reveal their preferences on individual items explicitly but may be willing to provide a single rating to a set of items, since it provides some level of information hiding. This article investigates two questions related to using set-level ratings in recommender systems. First, how users’ item-level ratings relate to their set-level ratings. Second, how collaborative filtering-based models for item-level rating prediction can take advantage of such set-level ratings. We have collected set-level ratings from active users of Movielens on sets of movies that they have rated in the past. Our analysis of these ratings shows that though the majority of the users provide the average of the ratings on a set’s constituent items as the rating on the set, there exists a significant number of users that tend to consistently either under- or over-rate the sets. We have developed collaborative filtering-based methods to explicitly model these user behaviors that can be used to recommend items to users. Experiments on real data and on synthetic data that resembles the under- or over-rating behavior in the real data demonstrate that these models can recover the overall characteristics of the underlying data and predict the user’s ratings on individual items.

Understanding the search tasks and search behavior of users is necessary for optimizing search engine results. While much work has been done on understanding the users in Web search, little knowledge is available about the search tasks and behavior of users in the E-Commerce (E-Com) search applications. In this paper, we share the first empirical study of the queries and search behavior of users in E-Com search by analyzing search log from a major E-Com search engine. The analysis results show that E-Com queries can be categorized into five categories, each with distinctive search behaviors: (1) Shallow Exploration Queries are short vague queries that a user may use initially in exploring the product space. (2) Targeted Purchase Queries are queries used by users to purchase items that they are generally familiar with, thus without much decision making. (3) Major-Item Shopping Queries are used by users to shop for a major item which is often relatively expensive and thus requires some serious exploration, but typically in a limited scope of choices. (4) Minor-Item Shopping Queries are used by users to shop for minor items that are generally not very expensive, but still require some exploration of choices. (5) Hard-Choice Shopping Queries are used by users who want to deeply explore all the candidate products before finalizing the choice often appropriate when multiple products must be carefully compared with each other. These five categories form a taxonomy for E-Com queries and can shed light on how we may develop customized search technologies for each type of search queries to improve search engine utility.

Customer Interaction Networks (CINs) are a natural framework for representing and mining customer interactions with E-Commerce search engines. Customer interactions begin with the submission of a query formulated based on an initial product intent, followed by a sequence of product engagement and query reformulation actions. Engagement with a product (e.g. clicks) indicates its relevance to the customer»s product intent. Reformulation to a new query indicates either dissatisfaction with current results, or an evolution in the customer»s product intent. Analyzing such interactions within and across sessions, enables us to discover various query-query and query-product relationships. In this work, we begin by studying the properties of CINs developed using Walmart.com»s product search logs. We observe that the properties exhibited by CINs make it possible to mine intent relationships between queries based purely on their structural information. We show how these relations can be exploited for a) clustering queries based on intents, b) significantly improve search quality for poorly performing queries, and c) identify the most influential (aka. »critical») queries whose performance have the highest impact on performance of other queries.

Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for Top-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.