On Interpretation and Measurement of Soft Attributes for Recommendation

We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques.publishedVersio

A Hybrid Travel Recommender System for Group Tourists

Travel recommender systems (TRSs) are developed as information filtering tools to provide travel decision-making support. They make personalised recommendations based on the user’s preferences. People tend to make group travel decisions based on trip-specific motivations. The current Group Travel Recommender Systems (GTRSs) exploit individual user’s preferences and make group recommendations by aggregating profiles or aggregating recommendations. Although aggregation is a straightforward way to combine the preferences of different group members, it has been critiqued on overlooking of the group dynamics. Interaction needs among tourists’ have a great influence on group travel preference. This proposed study explores a conceptual framework for a hybrid group travel recommender system based on this consideration

From Ranked Lists to Carousels: A Carousel Click Model

Carousel-based recommendation interfaces allow users to explore recommended items in a structured, efficient, and visually-appealing way. This made them a de-facto standard approach to recommending items to end users in many real-life recommenders. In this work, we try to explain the efficiency of carousel recommenders using a \emph{carousel click model}, a generative model of user interaction with carousel-based recommender interfaces. We study this model both analytically and empirically. Our analytical results show that the user can examine more items in the carousel click model than in a single ranked list, due to the structured way of browsing. These results are supported by a series of experiments, where we integrate the carousel click model with a recommender based on matrix factorization. We show that the combined recommender performs well on held-out test data, and leads to higher engagement with recommendations than a traditional single ranked list

Personalized Memory Transfer for Conversational Recommendation Systems

Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach

Toward a model for digital tool criticism: Reflection as integrative practice

In the past decade, an increasing set of digital tools has been developed with which digital sources can be selected, analyzed, and presented. Many tools go beyond key word search and perform different types of analysis, aggregation, mapping, and linking of data selections, which transforms materials and creates new perspectives, thereby changing the way scholars interact with and perceive their materials. These tools, together with the massive amount of digital and digitized data available for humanities research, put a strain on traditional humanities research methods. Currently, there is no established method of assessing the role of digital tools in the research trajectory of humanities scholars. There is no consensus on what questions researchers should ask themselves to evaluate digital sources beyond those of traditional analogue source criticism. This article aims to contribute to a better understanding of digital tools and the discussion of how to evaluate and incorporate them in research, based on findings from a digital tool criticism workshop held at the 2017 Digital Humanities Benelux conference. The overall goal of this article is to provide insight in the actual use and practice of digital tool criticism, offer a ready-made format for a workshop on digital tool criticism, give insight in aspects that play a role in digital tool criticism, propose an elaborate model for digital tool criticism that can be used as common ground for further conversations in the field, and finally, provide recommendations for future workshops, researchers, data custodians, and tool builders

Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models

Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors. Despite this, the focus of the majority of recommender research -- and most practical recommenders of any import -- is on the local, myopic optimization of the recommendations made to individual users. This comes at a significant cost to the long-term utility that recommenders could generate for its users. We argue that explicitly modeling the incentives and behaviors of all actors in the system -- and the interactions among them induced by the recommender's policy -- is strictly necessary if one is to maximize the value the system brings to these actors and improve overall ecosystem "health". Doing so requires: optimization over long horizons using techniques such as reinforcement learning; making inevitable tradeoffs in the utility that can be generated for different actors using the methods of social choice; reducing information asymmetry, while accounting for incentives and strategic behavior, using the tools of mechanism design; better modeling of both user and item-provider behaviors by incorporating notions from behavioral economics and psychology; and exploiting recent advances in generative and foundation models to make these mechanisms interpretable and actionable. We propose a conceptual framework that encompasses these elements, and articulate a number of research challenges that emerge at the intersection of these different disciplines