Analisis dan Implementasi House Recommendation Menggunakan Content-Based Recommender System

ABSTRAKSI: Tempat tinggal atau rumah siapa yang tidak membutuhkannya? Semua orang pasti membutuhkannya, saat seseorang sudah beranjak dewasa dan akan melangkah ke jenjang pernikahan. Bandung merupakan kota terbesar ke-empat di Indonesia. Namun, nama kota Bandung dapat dibilang terpopuler ke-dua setelah kota Jakarta. Oleh karena itu, penduduk kota Bandung saat ini banyak didominasi dari para pendatang. Sehingga kebutuhan rumah meningkat saat ini dan saat membeli rumah terkadang kita menginginkan rumah yang sesuai dengan keinginan. Untuk mengatasi permasalahan tersebut, pendekatan menggunakan recommender system dapat digunakan.Salah satu solusinya, Tugas Akhir ini membangun recommender system dengan content-based filtering yang mengimplementasikan algoritma Weigthed Product Method pada Multi-Criteria Decision Making. Algoritma Weigthed Product Method digunakan karena merupakan algoritma yang sangat sederhana dan mudah dalam pembangkitan alternatif rekomendasi. Dengan demikian, sistem akan memberikan rekomendasi rumah-rumah yang hampir mendekati kriteria dari costumer.Sesuai dengan hasil pengujian pada recommender system yang dibangun menunjukkan bahwa sistem memiliki performansi yang baik. Hal itu dibuktikan dengan adanya nilai performansi yang tinggi. Jenis kasus pengujian dan pemberian prioritas pada dua kriteria sangat berpengaruh pada nilai performansi.Kata Kunci : Rumah, Recommender System, Content-Based Filtering, Weigthed Product Method.ABSTRACT: Who do not need a home to stay? Everyone sure need a home to stay, if people become adult and they will be marry. Bandung is the fourth biggest town in Indonesia. But, It is said Bandung is the second popular town after Jakarta. That`s why, people of Bandung today is dominated by people outside of Bandung city. So that`s why the demand of the house is raising recently and when they buy a home, they want a home that suit with their desire. To solve that problem, approach using recommender system can be used.One of the solution, this final project build recommender system with content-based filtering which implement Weigthed Product Method algorithm on Multi-Criteria Decision Making. Weigthed Product Method algorithm is used because that algoritm is simple dan easy in building alternative recommendation.thus, the system will give the house recommendation which approach the costumer`s desire.According to the result test on recommender system which is build to show that the system have good performance. This is proven by the high value of performance value.the case of testing and different priority of criteria is highly affecting to the preformance value.Keyword: Home, Recommender System, Content-Based Filtering, Weigthed Product Method

COSMOS-7: Video-oriented MPEG-7 scheme for modelling and filtering of semantic content

MPEG-7 prescribes a format for semantic content models for multimedia to ensure interoperability across a multitude of platforms and application domains. However, the standard leaves it open as to how the models should be used and how their content should be filtered. Filtering is a technique used to retrieve only content relevant to user requirements, thereby reducing the necessary content-sifting effort of the user. This paper proposes an MPEG-7 scheme that can be deployed for semantic content modelling and filtering of digital video. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user

An MPEG-7 scheme for semantic content modelling and filtering of digital video

Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users

Closing the loop: assisting archival appraisal and information retrieval in one sweep

In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval

Personalizable Service Discovery in Pervasive Systems

Today, telecom providers are facing changing challenges. To stay ahead in the competition and provide market leading offerings, carriers need to enable a global ecosystem of third party independent application developers to deliver converged services. This is the aim of leveraging a open standardsbased service delivery platform. To identify and to cope with those challenges is the main target of the EU funded project IST DAIDALOS II. And a central point to satisfy the changing user needs is the provision of a well working, user friendly and personalized service discovery. This paper describes our work in the project on a middleware in a framework for pervasive service usage. We have designed an architecture for it, that enables full transparency to the user, grants high compatibility and extendability by a modular and pluggable conception and allows for interoperability with most known service discovery protocols. Our Multi-Protocol Service Discovery and the Four Phases Service Filtering concept enabling personalization should allow for the best possible results in service discovery

Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system