Mixed Information Flow for Cross-Domain Sequential Recommendations

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this article, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit . The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users’ current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that the proposed mixed information flow network is able to improve recommendation performance in different domains by modeling mixed information flow. In this article, we focus on the application of mixed information flow network s to a scenario with two domains, but the method can easily be extended to multiple domains.

Multi-interest Diversification for End-to-end Sequential Recommendation

Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation . Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.

Sequential Movie Genre Prediction Using Average Transition Probability with Clustering

In recent movie recommendations, one of the most important issues is to predict the user’s sequential behavior to be able to suggest the next movie to watch. However, capturing such sequential behavior is not easy because each user’s short-term or long-term behavior must be taken into account. For this reason, many research results show that the performance of recommending a specific movie is not good in a sequential recommendation. In this paper, we propose a cluster-based method for classifying users with similar movie purchase patterns and a movie genre prediction algorithm rather than the movie itself considering their short-term and long-term behaviors. The movie genre prediction does not recommend a specific movie, but it predicts the genre for the next movie to watch in consideration of each user’s preference for the movie genre based on the genre included in the movie. Using this, it will be possible to provide appropriate guidelines for recommending movies including the genres to users who tend to prefer a specific genre. In particular, in this study, users with similar genre preferences are organized into clusters to recommend genres. For clusters that do not have relatively specific tendencies, genre prediction is performed by appropriately trimming genres that are not necessary for recommendation in order to improve performance. We evaluate our method on well-known movie data sets and qualitatively determine that it captures personalized dynamics and is able to make meaningful recommendations.

Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors

Design thinking is essential to the success of a design process as it helps achieve the design goal by guiding design decision-making. Therefore, fundamentally understanding design thinking is vital for improving design methods, tools and theories. However, interpreting design thinking is challenging because it is a cognitive process that is hidden and intangible. In this paper, we represent design thinking as an intermediate layer between human designers’ thought processes and their design behaviors. To do so, this paper first identifies five design behaviors based on the current design theories. These behaviors include design action preference, one-step sequential behavior, contextual behavior, long-term sequential behavior, and reflective thinking behavior. Next, we develop computational methods to characterize each of the design behaviors. Particularly, we use design action distribution, first-order Markov chain, Doc2Vec, bi-directional LSTM autoencoder, and time gap distribution to characterize the five design behaviors. The characterization of the design behaviors through embedding techniques is essentially a latent representation of the design thinking, and we refer to it as design embeddings. After obtaining the embedding, an X-mean clustering algorithm is adopted to each of the embeddings to cluster designers. The approach is applied to data collected from a high school solar system design challenge. The clustering results show that designers follow several design patterns according to the corresponding behavior, which corroborates the effectiveness of using design embedding for design behavior clustering. The extraction of design embedding based on the proposed approach can be useful in other design research, such as inferring design decisions, predicting design performance, and identifying design actions identification.

Feline communication strategies when presented with an unsolvable task: the attentional state of the person matters

Research on social cognitive ability in domestic cats is limited. The current study investigated social referencing in cats when exposed to first, a solvable, and then, an unsolvable scenario (i.e., reachable and unreachable treats) in the presence of either an attentive or an inattentive caregiver. Cats expressed more gaze alternation (P = 0.013), but less interaction with the caregiver (P = 0.048) and approached the treat container less frequently (P = 0.017) during the unsolvable test, compared to the solvable test. When in the presence of an attentive caregiver, cats initiated first gaze at the caregiver faster (P = 0.001); gazed at the caregiver for longer (P = 0.034); and approached the treat more frequently (P = 0.040), compared to when the caregiver was inattentive. Significant interaction was observed between test and caregiver’s attentional state on the expression of sequential behavior, a type of showing behavior. Cats exhibited this behavior marginally more with attentive caregivers, compared to inattentive caregivers, but only during the unsolvable test. There was a decrease in sequential behavior during the unsolvable test, compared to solvable test, but this was only seen with inattentive caregivers (P = 0.018). Our results suggest that gaze alternation is a behavior reliably indicating social referencing in cats and that cats’ social communication with humans is affected by the person’s availability for visual interaction.

Nigrostriatal Dopamine Signals Sequence-Specific Action-Outcome Prediction Errors

ABSTRACTDopamine has been suggested to encode cue-reward prediction errors during Pavlovian conditioning. While this theory has been widely applied to reinforcement learning concerning instrumental actions, whether dopamine represents action-outcome prediction errors and how it controls sequential behavior remain largely unknown. Here, by training mice to perform optogenetic intracranial self-stimulation, we examined how self-initiated goal-directed behavior influences nigrostriatal dopamine transmission during single as well as sequential instrumental actions. We found that dopamine release evoked by direct optogenetic stimulation was dramatically reduced when delivered as the consequence of the animal’s own action, relative to non-contingent passive stimulation. This action-induced dopamine suppression was specific to the reinforced action, temporally restricted to counteract the expected outcome, and exhibited sequence-selectivity consistent with hierarchical control of sequential behavior. Together these findings demonstrate that nigrostriatal dopamine signals sequence-specific prediction errors in action-outcome associations, with fundamental implications for reinforcement learning and instrumental behavior in health and disease.

Topological Stationarity and Precompactness of Probability Measures

We prove the precompactness of a collection of Borel probability measures over an arbitrary metric space precisely under a new legitimate notion, which we term $topological$ $stationarity$, regulating the sequential behavior of Borel probability measures directly in terms of the open sets. Thus the important direct part of Prokhorov's theorem, which permeates the weak convergence theory, admits a new version with the original and sole assumption --- tightness --- replaced by topological stationarity. Since, as will be justified, our new condition is not vacuous and is logically independent of tightness, our result deepens the understanding of the connection between precompactness of Borel probability measures and metric topologies.