Targeted Mobile Advertisement in the IP Multimedia Subsystem

2011 ◽  
pp. 279-300
Author(s):  
C. Tselios ◽  
H. Perkuhn ◽  
K. Vandikas ◽  
M. Kampmann
Keyword(s):  
Contextual Information ◽  
Vector Space Model ◽  
Mobile Network ◽  
User Profile ◽  
Location Information ◽  
Ip Multimedia Subsystem ◽  
Contextual Data ◽  
Space Model ◽  
User Interests ◽  
Personalized Advertisement

This chapter provides an overview on targeted advertisement in the IP Multimedia Subsystem (IMS). A new entity called Personalization and Advertisement Insertion Logic (PAIL) is introduced, which enables a mobile network operator to exploit contextual data stored in its network for personalized advertisement selection. PAIL combines location information with user profile information in order to select the best match from a pool of advertisement clips. This selection is based on the Vector Space Model. For the evaluation of this framework a series of tests with users were executed. These tests show that using contextual information from the IMS network a subjective better match of advertisement clips with user interests is achievable.

scholarly journals Personalizing Type-Based Facet Ranking Using BERT Embeddings

2021 ◽  
Author(s):  
Esraa Ali ◽  
Annalina Caputo ◽  
Séamus Lawless ◽  
Owen Conlan
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
User Profile ◽  
Leaf Node ◽  
Information Space ◽  
Faceted Search ◽  
Space Model ◽  
Individual Leaf ◽  
Structured Information ◽  
Profile Vector

In Faceted Search Systems (FSS), users navigate the information space through facets, which are attributes or meta-data that describe the underlying content of the collection. Type-based facets (aka t-facets) help explore the categories associated with the searched objects in structured information space. This work investigates how personalizing t-facet ranking can minimize user effort to reach the intended search target. We propose a lightweight personalisation method based on Vector Space Model (VSM) for ranking the t-facet hierarchy in two steps. The first step scores each individual leaf-node t-facet by computing the similarity between the t-facet BERT embedding and the user profile vector. In this model, the user’s profile is expressed in a category space through vectors that capture the users’ past preferences. In the second step, this score is used to re-order and select the sub-tree to present to the user. The final ranked tree reflects the t-facet relevance both to the query and the user profile. Through the use of embeddings, the proposed method effectively handles unseen facets without adding extra processing to the FSS. The effectiveness of the proposed approach is measured by the user effort required to retrieve the sought item when using the ranked facets. The approach outperformed existing personalization baselines.

scholarly journals Time-Aware and Grey Incidence Theory Based User Interest Modeling for Document Recommendation

2015 ◽  
Vol 15 (2) ◽  
pp. 36-52 ◽  
Author(s):  
Shulin Cheng ◽  
Yuejun Liu
Keyword(s):  
Vector Space Model ◽  
Vector Model ◽  
User Interest ◽  
Space Model ◽  
User Interests ◽  
The Past ◽  
Coefficient Vector ◽  
Interest Model ◽  
User Interest Model ◽  
Time Aware

Abstract Document recommendation involves the recommendation of documents similar to those that a user has preferred in the past. The Vector Space Model (VSM) is commonly adopted to denote the document objects and user interests. The user interests are extracted from the documents that a user has browsed. The interest degree of the user is calculated using the TF-IDF method, but the time factor is not considered. The recent documents that a user has browsed embody much more his/her interests. This study proposes a time-aware and grey incidence theory based user interest model to improve document recommendation. First, the time-aware user interest model is proposed based on the analysis of the user interests, document objects and user interest knowledge table. Second, a coefficient vector model of the user interest degree is designed using the grey incidence theory to differentiate the main from the minor user interests. The time-aware and grey incidence theory based user interest model is then exploited to produce document recommendations. Finally, the experiment and evaluation metrics are studied. The results show that the model proposed outperforms other related models and recommends more accurate documents to the users.

Improving Document Retrieval with Spelling Correction for Weak and Fabricated Indonesian-Translated Hadith

2020 ◽  
Vol 4 (3) ◽  
pp. 551-557
Author(s):  
Muhammad zaky ramadhan ◽  
Kemas Muslim Lhaksmana
Keyword(s):  
Retrieval System ◽  
Islamic Law ◽  
Vector Space Model ◽  
Document Retrieval ◽  
The Internet ◽  
Average Precision ◽  
Spelling Correction ◽  
Space Model ◽  
The Mean ◽  
The Web

Hadith has several levels of authenticity, among which are weak (dhaif), and fabricated (maudhu) hadith that may not originate from the prophet Muhammad PBUH, and thus should not be considered in concluding an Islamic law (sharia). However, many such hadiths have been commonly confused as authentic hadiths among ordinary Muslims. To easily distinguish such hadiths, this paper proposes a method to check the authenticity of a hadith by comparing them with a collection of fabricated hadiths in Indonesian. The proposed method applies the vector space model and also performs spelling correction using symspell to check whether the use of spelling check can improve the accuracy of hadith retrieval, because it has never been done in previous works and typos are common on Indonesian-translated hadiths on the Web and social media raw text. The experiment result shows that the use of spell checking improves the mean average precision and recall to become 81% (from 73%) and 89% (from 80%), respectively. Therefore, the improvement in accuracy by implementing spelling correction make the hadith retrieval system more feasible and encouraged to be implemented in future works because it can correct typos that are common in the raw text on the Internet.

Aplikasi Deteksi Kemiripan Tugas Paper

2017 ◽  
Vol 15 (2) ◽  
pp. 5
Author(s):  
Anthony Anggrawan ◽  
Azhari
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Mean Average Precision ◽  
Average Precision ◽  
Information Searching ◽  
Space Model ◽  
Model Method

Information searching based on users’ query, which is hopefully able to find the documents based on users’ need, is known as Information Retrieval. This research uses Vector Space Model method in determining the similarity percentage of each student’s assignment. This research uses PHP programming and MySQL database. The finding is represented by ranking the similarity of document with query, with mean average precision value of 0,874. It shows how accurate the application with the examination done by the experts, which is gained from the evaluation with 5 queries that is compared to 25 samples of documents. If the number of counted assignments has higher similarity, thus the process of similarity counting needs more time, it depends on the assignment’s number which is submitted.

Aplikasi Rekomendasi Buku Pada Katalog Perpustakaan Universitas Multimedia Nusantara Menggunakan Vector Space Model

2018 ◽  
Vol 9 (2) ◽  
pp. 97-105
Author(s):  
Richard Firdaus Oeyliawan ◽  
Dennis Gunawan
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Vector Model ◽  
Library Management ◽  
Space Model ◽  
Library Management System ◽  
Index Terms ◽  
Library Catalogue ◽  
Language Sample ◽  
F Measure

Library is one of the facilities which provides information, knowledge resource, and acts as an academic helper for readers to get the information. The huge number of books which library has, usually make readers find the books with difficulty. Universitas Multimedia Nusantara uses the Senayan Library Management System (SLiMS) as the library catalogue. SLiMS has many features which help readers, but there is still no recommendation feature to help the readers finding the books which are relevant to the specific book that readers choose. The application has been developed using Vector Space Model to represent the document in vector model. The recommendation in this application is based on the similarity of the books description. Based on the testing phase using one-language sample of the relevant books, the F-Measure value gained is 55% using 0.1 as cosine similarity threshold. The books description and variety of languages affect the F-Measure value gained. Index Terms—Book Recommendation, Porter Stemmer, SLiMS Universitas Multimedia Nusantara, TF-IDF, Vector Space Model

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