Combining long-term and short-term user interest for personalized hashtag recommendation

PurposeThe user interest in content searching in the web will be changed over by time.Design/methodology/approachThe system is in need to find the content of user over the temporal aspects.FindingsSo, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.Research limitations/implicationsSo, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.Practical implicationsIn this work, fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios.Social implicationsIn this proposed work, both the long-term and short-term interest are evaluated using the specialized user interface designed to retrieve the user interest based on the user searching activities.Originality/valueThis work also categorizes the future needs of users using this proposed system.

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A PERSONALIZED IMAGE RETRIEVAL BASED ON USER INTEREST MODEL

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001410008019 ◽

2010 ◽

Vol 24 (03) ◽

pp. 401-419 ◽

Cited By ~ 7

Author(s):

JING ZHANG ◽

LI ZHUO ◽

LANSUN SHEN ◽

LIN HE

Keyword(s):

Image Retrieval ◽

User Satisfaction ◽

Clustering Algorithm ◽

Visual Features ◽

Semantic Features ◽

User Interest ◽

Short Term ◽

Interest Model ◽

User Interest Model

In order to narrow the semantic gap, user interest model plays an important role in personalized image retrieval. A novel personalized image retrieval approach based on user interest model is proposed in this study. User interest model is developed on the basis of short-tem and long-term interests. (1) Short-term interests are represented by collecting visual and semantic features. Visual features are collected by MARS relevance feedback. Semantic features are constructed by building a mapping from image low-level visual features to high-level semantic features on the basis of SVM. (2) Long-term interests are inferred by inference engine from the collected short-term interests. Long-term visual features are collected by the nonlinear gradual forgetting interest inference algorithm and semantic features are obtained by clustering algorithm. After applying to image retrieval, experimental results show that the average recall/precision is significantly improved and a better user satisfaction rate is achieved as well. Furthermore, it demonstrates our model can be efficiently adapted to user interests and matches personalized image retrieval.

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Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention

Mathematical Problems in Engineering ◽

10.1155/2019/7586589 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13

Author(s):

Qiaoqiao Tan ◽

Fang’ai Liu

Keyword(s):

User Behavior ◽

User Interest ◽

Short Term ◽

User Interests ◽

User Behaviors ◽

Current State ◽

Proposed Model ◽

Dependence Problem ◽

Behavior Sequences

Recommendations based on user behavior sequences are becoming more and more common. Some studies consider user behavior sequences as interests directly, ignoring the mining and representation of implicit features. However, user behaviors contain a lot of information, such as consumption habits and dynamic preferences. In order to better locate user interests, this paper proposes a Bi-GRU neural network with attention to model user’s long-term historical preferences and short-term consumption motivations. First, a Bi-GRU network is established to solve the long-term dependence problem in sequences, and attention mechanism is introduced to capture user interest changes related to the target item. Then, user’s short-term interaction trajectory based on self-attention is modeled to distinguish the importance of each potential feature. Finally, combined with long-term and short-term interests, the next behavior is predicted. We conducted extensive experiments on Amazon and MovieLens datasets. The experimental results demonstrate that the proposed model outperforms current state-of-the-art models in Recall and NDCG indicators. Especially in MovieLens dataset, compared with other RNN-based models, our proposed model improved at least 2.32% at Recall@20, which verifies the effectiveness of modeling long-term and short-term interest of users, respectively.

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A Hybrid Approach for Personalized News Recommendation in a Mobility Scenario Using Long-Short User Interest

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213017600120 ◽

2017 ◽

Vol 26 (02) ◽

pp. 1760012 ◽

Cited By ~ 2

Author(s):

Paula Viana ◽

Márcio Soares

Keyword(s):

Recommendation System ◽

Hybrid Approach ◽

User Interest ◽

Huge Amount ◽

Short Term ◽

Pyramid Model ◽

News Recommendation ◽

Different Levels

Access to information has been made easier in different domains that range from multimedia content, books, music, news, etc. To deal with the huge amount of alternatives, recommendation systems have been often used as a solution to filter the options and provide suggestions of items that might be of interest to an user. The news domain introduces additional challenges due not only to the large amount of new items produced daily but also due to their ephemeral timelife. In this paper, a news recommendation system which combines content-based and georeferenced techniques in a mobility scenario, is proposed. Taking into account the volatility of the information, short-term and long-term user profiles are considered and implicitly built. Besides tracking users’ clicks, the system infers different levels of interest an article has by tracking and weighting each action in the system and in social networks. Impact of the different fields that make up a news is also taken into account by following the inverted pyramid model that assumes different levels of importance to each paragraph of the article. The solution was tested with a population of volunteers and results indicate that the quality of the recommendation approach is acknowledged by the users.

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Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

Behavioral and Brain Sciences ◽

10.1017/s0140525x15000928 ◽

2016 ◽

Vol 39 ◽

Author(s):

Mary C. Potter

Keyword(s):

Working Memory ◽

Rapid Serial Visual Presentation ◽

Language Comprehension ◽

Short Term Memory ◽

Visual Presentation ◽

Long Term Memory ◽

Short Term ◽

Term Memory ◽

Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

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Ultrastructural investigations of MDL 19,660-induced effects on the canine platelet and megakaryocyte

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100159412 ◽

1990 ◽

Vol 48 (3) ◽

pp. 374-375

Author(s):

D.E. Loudy ◽

J. Sprinkle-Cavallo ◽

J.T. Yarrington ◽

F.Y. Thompson ◽

J.P. Gibson

Keyword(s):

Antidepressant Drug ◽

Beagle Dogs ◽

Long Term Effects ◽

Short Term ◽

Platelet Counts ◽

Toxicological Studies ◽

Induced Aggregation ◽

Internal Architecture

Previous short term toxicological studies of one to two weeks duration have demonstrated that MDL 19,660 (5-(4-chlorophenyl)-2,4-dihydro-2,4-dimethyl-3Hl, 2,4-triazole-3-thione), an antidepressant drug, causes a dose-related thrombocytopenia in dogs. Platelet counts started to decline after two days of dosing with 30 mg/kg/day and continued to decrease to their lowest levels by 5-7 days. The loss in platelets was primarily of the small discoid subpopulation. In vitro studies have also indicated that MDL 19,660: does not spontaneously aggregate canine platelets and has moderate antiaggregating properties by inhibiting ADP-induced aggregation. The objectives of the present investigation of MDL 19,660 were to evaluate ultrastructurally long term effects on platelet internal architecture and changes in subpopulations of platelets and megakaryocytes.Nine male and nine female beagle dogs were divided equally into three groups and were administered orally 0, 15, or 30 mg/kg/day of MDL 19,660 for three months. Compared to a control platelet range of 353,000- 452,000/μl, a doserelated thrombocytopenia reached a maximum severity of an average of 135,000/μl for the 15 mg/kg/day dogs after two weeks and 81,000/μl for the 30 mg/kg/day dogs after one week.

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Comparison of Auditory, Language, Memory, and Attention Abilities in Children With and Without Listening Difficulties

American Journal of Audiology ◽

10.1044/2020_aja-20-00018 ◽

2020 ◽

Vol 29 (4) ◽

pp. 710-727

Author(s):

Beula M. Magimairaj ◽

Naveen K. Nagaraj ◽

Alexander V. Sergeev ◽

Natalie J. Benafield

Keyword(s):

Auditory Processing ◽

Short Term Memory ◽

Group Differences ◽

Long Term Memory ◽

Short Term ◽

Significant Group ◽

Term Memory ◽

Memory And Attention ◽

Speed And Accuracy

Objectives School-age children with and without parent-reported listening difficulties (LiD) were compared on auditory processing, language, memory, and attention abilities. The objective was to extend what is known so far in the literature about children with LiD by using multiple measures and selective novel measures across the above areas. Design Twenty-six children who were reported by their parents as having LiD and 26 age-matched typically developing children completed clinical tests of auditory processing and multiple measures of language, attention, and memory. All children had normal-range pure-tone hearing thresholds bilaterally. Group differences were examined. Results In addition to significantly poorer speech-perception-in-noise scores, children with LiD had reduced speed and accuracy of word retrieval from long-term memory, poorer short-term memory, sentence recall, and inferencing ability. Statistically significant group differences were of moderate effect size; however, standard test scores of children with LiD were not clinically poor. No statistically significant group differences were observed in attention, working memory capacity, vocabulary, and nonverbal IQ. Conclusions Mild signal-to-noise ratio loss, as reflected by the group mean of children with LiD, supported the children's functional listening problems. In addition, children's relative weakness in select areas of language performance, short-term memory, and long-term memory lexical retrieval speed and accuracy added to previous research on evidence-based areas that need to be evaluated in children with LiD who almost always have heterogenous profiles. Importantly, the functional difficulties faced by children with LiD in relation to their test results indicated, to some extent, that commonly used assessments may not be adequately capturing the children's listening challenges. Supplemental Material https://doi.org/10.23641/asha.12808607

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