Tejpalsingh Siledar, Swaroop Nath, Sankara Muddu, Rupasai Rangaraju, Swaprava Nath, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to provide fair earning opportunities to them. The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products.

Recommender systems are able to produce a list of recommended items tailored to user preferences, while the end user is the only stakeholder in these traditional system. However, there could be multiple stakeholders in several applications domains (e.g., e-commerce, movies, music). Recommendations are necessary to be produced by balancing the needs of different stakeholders. First session of this tutorial introduces multi-stakeholder recommender systems (MSRS) with several case studies, and discusses the corresponding methods and challenges in MSRS. Reviews in an e-commerce platform may be mined to address cold-start problem and to generate explanations. Our earlier tutorial covered aspect-based sentiment analysis of products and topic models/distributed representations that bridge vocabulary gap between user reviews and product descriptions. Focus in the second session of this tutorial instead is on recent neural methods for review text mining - covering hands-on code for its use to enhance product recommendation. Each section will introduce topics from various mechanism (e.g., attention) and task (e.g., review ranking) perspectives, present cutting-edge research and a walk-through of programs executed on Jupyter notebook using real-world data sets.

Outfit recommendation automatically pairs user-specified reference clothing with the most suitable complement from online shops. Wearing aesthetically is a criterion for matching such fashion items. Fashion style tells a lot about one's personality and emerges from how people assemble clothing outfit from seemingly disjoint items into a cohesive concept. Experts share fashion tips showcasing their compositions to public where each item has both an image and textual meta-data. Also, retrieving products from online shopping catalogs in response to such real-world image query is essential for outfit recommendation. Our earlier tutorial focused on style and compatibility in fashion recommendation mostly based on metric and deep learning approaches. Herein, we cover several other aspects of fashion recommendation using visual signals (e.g., cross-scenario retrieval, attribute classification) and combine text input (e.g., interpretable embedding) as well. Each section concludes walking through programs executed on Jupyter workstation using real-world data sets.

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).

Many users of e-commerce portals commonly use customer reviews for making purchase decisions. But a product may have tens or hundreds of diverse reviews leading to information overload on the customer. The main objective of our work is to develop a recommendation system to recommend a subset of reviews that have high content score and good coverage over different aspects of the product along with their associated sentiments. We address the challenge which arises due to the fact that similar aspects are mentioned in different reviews using different natural language expressions. We use vector representations to identify mentions of similar aspects and map them with aspects mentioned in product features specifications. Review helpfulness score may act as a proxy for the quality of reviews, but new reviews do not have any helpfulness score. We address the cold start problem by using a dynamic convolutional neural network to estimate the quality score from review content. The system is evaluated on datasets from Amazon and Flipkart and is found to be more effective than the competing methods.

Distributed applications that access persistent objects must maintain object state consistency even when failures are encountered during the manipulation of such objects. The basic transaction model, which has been implemented by several systems to ensure consistent executions of distributed applications, is not flexible enough to meet the requirements of many complex distributed applications. This has also been recognized for advanced database applications and, as a result, extended transaction models have been developed. We argue that distributed applications that manipulate long-lived data can benefit from such transaction models. We take an approach which views the various transaction models as policies for building robust applications. Thus, we advocate that the system implement several transaction models. A robust application can be programmed in such a system using a combination of several transaction models to meet its consistency requirements. We use applications from the domain of computer-supported cooperative work to motivate such an approach. We also develop a set of system-level mechanisms which can be used to implement multiple transaction models in a uniform manner. These mechanisms are used to implement nested, split, and cooperating transaction models. A prototype system that has been implemented is described to demonstrate the feasibility of this approach.

Traditional transaction models ensure robustness for distributed applications through the properties of view and failure atomicity. It has generally been felt that such atomicity properties are restrictive for a wide range of application domains; this is particularly true for robust, collaborative applications because such applications have concurrent components that are inherently long-lived and that cooperate. Recent advances in extended transaction models can be exploited to structure long-lived and cooperative computations. Applications can use a combination of such models to achieve the desired degree of robustness; hence, we develop a system which can support a number of flexible transaction models, with correctness criteria that extend or relax serializability. We analyze two concrete CSCW applications-collaborative editor and meeting scheduler. We show how a combination of two extended transaction models, that promote split and cooperating actions, facilitates robust implementations of these collaborative applications. Thus, we conclude that a system that implements multiple transaction models provides flexible support for building robust collaborative applications.