206,959 research outputs found

    Deep Item-based Collaborative Filtering for Top-N Recommendation

    Full text link
    Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

    SAIN: Self-Attentive Integration Network for Recommendation

    Full text link
    With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%Comment: SIGIR 201

    Visual marking and change blindness : moving occluders and transient masks neutralize shape changes to ignored objects

    Get PDF
    Visual search efficiency improves by presenting (previewing) one set of distractors before the target and remaining distractor items (D. G. Watson & G. W. Humphreys, 1997). Previous work has shown that this preview benefit is abolished if the old items change their shape when the new items are added (e.g., D. G. Watson & G. W. Humphreys, 2002). Here we present 5 experiments that examined whether such object changes are still effective in recapturing attention if the changes occur while the previewed objects are occluded or masked. Overall, the findings suggest that masking transients are effective in preventing both object changes and the presentation of new objects from capturing attention in time-based visual search conditions. The findings are discussed in relation to theories of change blindness, new object capture, and the ecological properties of time-based visual selection. (PsycINFO Database Record (c) 2010 APA, all rights reserved

    Latent Embeddings for Collective Activity Recognition

    Full text link
    Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ- ous state-of-the-art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural de- pendencies among individuals involved in a collective sce- nario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interac- tions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble atten- tion mechanism during embedding for achieving more com- pact representations. We evaluate our method on three col- lective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-the- art methods in our experiments.Comment: 6pages, accepted by IEEE-AVSS201

    Do Physicians Respond to Liability Standards?

    Get PDF
    In this paper, we explore the sensitivity in the clinical decisions of physicians to the standards of care expected of them under the law, drawing on the abandonment by states over time of rules holding physicians to standards determined by local customs and the contemporaneous adoption of national-standard rules. Using data on broad rates of surgical interventions at the county-by-year level from the Area Resource File, we find that local surgery rates converge towards national surgery rates upon the adoption of national-standard rules. Moreover, we find that these effects are more pronounced among rural counties

    Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation

    Get PDF
    Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches

    Attentional capture by entirely irrelevant distractors

    Get PDF
    Studies of attentional capture often question whether an irrelevant distractor will capture attention or be successfully ignored (e.g., Folk & Remington, 1998). Here we establish a new measure of attentional capture by distractors that are entirely irrelevant to the task in terms of visual appearance, meaning, and location (colourful cartoon figures presented in the periphery while subjects perform a central letter-search task). The presence of such a distractor significantly increased search RTs, suggesting it captured attention despite its task-irrelevance. Such attentional capture was found regardless of whether the search target was a singleton or not, and for both frequent and infrequent distractors, as well as for meaningful and meaningless distractor stimuli, although the cost was greater for infrequent and meaningful distractors. These results establish stimulus-driven capture by entirely irrelevant distractors and thus provide a demonstration of attentional capture that is more akin to distraction by irrelevant stimuli in daily life

    Visual marking and facial affect : can an emotional face be ignored?

    Get PDF
    Previewing a set of distractors allows them to be ignored in a subsequent visual search task (Watson & Humphreys, 1997). Seven experiments investigated whether this preview benefit can be obtained with emotional faces, and whether negative and positive facial expressions differ in the extent to which they can be ignored. Experiments 1–5 examined the preview benefit with neutral, negative, and positive previewed faces. These results showed that a partial preview benefit occurs with face stimuli, but that the valence of the previewed faces has little impact. Experiments 6 and 7 examined the time course of the preview benefit with valenced faces. These showed that negative faces were more difficult to ignore than positive faces, but only at short preview durations. Furthermore, a full preview benefit was not obtained with face stimuli even when the preview duration was extended up to 3 s. The findings are discussed in terms of the processes underlying the preview benefit, their ecological sensitivity, and the role of emotional valence in attentional capture and guidance
    corecore