Item Recommendation with Evolving User Preferences and Experience

Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in the user's writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the user's maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a user's interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels

University of Strathclyde at TREC HARD

The motivation behind the University of Strathclyde's approach to this years HARD track was inspired from previous experiences by other participants, in particular research by [1], [3] and [4]. A running theme throughout these papers was the underlying hypothesis that a user's familiarity in a topic (i.e. their previous experience searching a subject), will form the basis for what type or style of document they will perceive as relevant. In other words, the user's context with regards to their previous search experience will determine what type of document(s) they wish to retrieve

SoftHand at the CYBATHLON: A user's experience

Background: Roughly one-quarter of upper limb prosthesis users reject their prosthesis. Reasons for rejection range from comfort, to cost, aesthetics, function, and more. This paper follows a single user from training with and testing of a novel upper-limb myoelectric prosthesis (the SoftHand Pro) for participation in the CYBATHLON rehearsal to training for and competing in the CYBATHLON 2016 with a figure-of-nine harness controlled powered prosthesis (SoftHand Pro-H) to explore the feasibility and usability of a flexible anthropomorphic prosthetic hand. Methods: The CYBATHLON pilot took part in multiple in-lab training sessions with the SoftHand Pro and SoftHand Pro-H; these sessions focused on basic control and use of the prosthetic devices and direct training of the tasks in the CYBATHLON. He used these devices in competition in the Powered Arm Prosthesis Race in the CYBATHLON rehearsal and 2016 events. Results: In training for the CYBATHLON rehearsal, the subject was able to quickly improve performance with the myoelectric SHP despite typically using a body-powered prosthetic hook. The subject improved further with additional training using the figure-of-nine harness-controlled SHPH in preparation for the CYBATHLON. The Pilot placed 3rd (out of 4) in the rehearsal. In the CYBATHLON, he placed 5th (out of 12) and was one of only two pilots who successfully completed all tasks in the competition, having the second-highest score overall. Conclusions: Results with the SoftHand Pro and Pro-H suggest it to be a viable alternative to existing anthropomorphic hands and show that the unique flexibility of the hand is easily learned and exploited

Context generation and information retrieval

The interaction between a user and an information retrieval system can be viewed as a dialogue in which both participants are trying to interpret the others' actions in the light of previous experience. The sys- tem then must try to generate a context in which to interpret the user's response to the presented mate- rial. This notion of context operates on a principle of relevance. Information that the system believes is relevant to the user, or that the user has indicated as relevant will form the basis of the system's notion of the context. This paper presents a way of represent- ing a context that can use both the systems knowl- edge about itself and the user's response to generate a view of the retrieval session

A model for mobile content filtering on non-interactive recommendation systems

To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems

Hot Streaks on Social Media

Measuring the impact and success of human performance is common in various disciplines, including art, science, and sports. Quantifying impact also plays a key role on social media, where impact is usually defined as the reach of a user's content as captured by metrics such as the number of views, likes, retweets, or shares. In this paper, we study entire careers of Twitter users to understand properties of impact. We show that user impact tends to have certain characteristics: First, impact is clustered in time, such that the most impactful tweets of a user appear close to each other. Second, users commonly have 'hot streaks' of impact, i.e., extended periods of high-impact tweets. Third, impact tends to gradually build up before, and fall off after, a user's most impactful tweet. We attempt to explain these characteristics using various properties measured on social media, including the user's network, content, activity, and experience, and find that changes in impact are associated with significant changes in these properties. Our findings open interesting avenues for future research on virality and influence on social media.Comment: Accepted as a full paper at ICWSM 2019. Please cite the ICWSM versio

The Case for Graph-Based Recommendations

Recommender systems have been intensively used to create personalised profiles, which enhance the user experience. In certain areas, such as e-learning, this approach is short-sighted, since each student masters each concept through different means. The progress from one concept to the next, or from one lesson to another, does not necessarily follow a fixed pattern. Given these settings, we can no longer use simple structures (vectors, strings, etc.) to represent each user's interactions with the system, because the sequence of events and their mapping to user's intentions, build up into more complex synergies. As a consequence, we propose a graph-based interpretation of the problem and identify the challenges behind (a) using graphs to model the users' journeys and hence as the input to the recommender system, and (b) producing recommendations in the form of graphs of actions to be taken