Motives or goals are recognized in psychology literature as the most fundamental drive that explains and predicts why people do what they do, including when they browse the web. Although providing enormous value, these higher-ordered goals are often unobserved, and little is known about how to leverage such goals to assist people’s browsing activities. This paper proposes to take a new approach to address this problem, which is fulfilled through a novel neural framework, Goal-directed Web Browsing  (GoWeB). We adopt a psychologically-sound taxonomy of higher-ordered goals and learn to build their representations in a structure-preserving manner. Then we incorporate the resulting representations for enhancing the experiences of common activities people perform on the web. Experiments on large-scale data from Microsoft Edge web browser show that GoWeB significantly outperforms competitive baselines for in-session web page recommendation, re-visitation classification, and goal-based web page grouping. A follow-up analysis further characterizes how the variety of human motives can affect the difference observed in human behavioral patterns.

Full text in ACM Digital Library

Building recommendation systems for enterprise software has many unique challenges that are different from consumer-facing systems. When applied to different organizations, the data used to power those recommendation systems vary substantially in both quality and quantity due to differences in their operational practices, marketing strategies, and targeted audiences. At Salesforce, as a cloud provider of such a system with data across many different organizations, naturally, it makes sense to pool data from different organizations to build a model that combines all values from different brands. However, multiple issues like how do we make sure a model trained with pooled data can still capture customer specific characteristics, how do we design the system to handle those data responsibly and ethically, i.e., respecting contractual agreements with our clients, legal and compliance requirements, and the privacy of all the consumers. In this proposal, We present a framework that not only utilizes enriched user-level data across organizations, but also boosts business-specific characteristics in generating personal recommendations. We will also walk through key privacy considerations when designing such a system.

Full text in ACM Digital Library

The role of recommendation systems in the diversity of content consumption on platforms is a much-debated issue. The quantitative state of the art often overlooks the existence of individual attitudes toward guidance, and eventually of different categories of users in this regard. Focusing on the case of music streaming, we analyze the complete listening history of about 9k users over one year and demonstrate that there is no blanket answer to the intertwinement of recommendation use and consumption diversity: it depends on users. First we compute for each user the relative importance of different access modes within their listening history, introducing a trichotomy distinguishing so-called ‘organic’ use from algorithmic and editorial guidance. We thereby identify four categories of users. We then focus on two scales related to content diversity, both in terms of dispersion – how much users consume the same content repeatedly – and popularity – how popular is the content they consume. We show that the two types of recommendation offered by music platforms – algorithmic and editorial – may drive the consumption of more or less diverse content in opposite directions, depending also strongly on the type of users. Finally, we compare users’ streaming histories with the music programming of a selection of popular French radio stations during the same period. While radio programs are usually more tilted toward repetition than users’ listening histories, they often program more songs from less popular artists. On the whole, our results highlight the nontrivial effects of platform-mediated recommendation on consumption, and lead us to speak of ‘filter niches’ rather than ‘filter bubbles’. They hint at further ramifications for the study and design of recommendation systems.

Full text in ACM Digital Library