User adaptive systems

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As it is a website which deals with music, and it would have so many clients (members) with different kind of musics such as : Rock, Soul, House, Hip-Hop, Blues, etc. The most appropriate user-adaptive features for this website are:


These user-adaptive features are suitable for this specific case, because it includes all themain personalisation features to the website, as it will deals with different users and it might need to improve its interface in a way which the user finds easier to use it. We also would need to get some informations to help us to suggest some albums that a determined user might like. It is necessary to give some help to the user of how to use the application or perform some actions itself. Dealsthe real world interaction in the website.


With this feature we will enable the system (site) to get as much information we can get from the user(client) and about the albums, as well as the artists. We also will let users to access as much informations we can give about the site and the world of music, such that the website will not be only for sale, butfor keep clients up to date in the music world.
In order to get this we use techniques for collection and analysing information. And it can be achieved by collecting visitor information, filtering, and developing recommendations.
Collecting visitor information – we can develop a profile that describes a site visitor interests, entitlements, purchases, etc. For it we can use some techniques whichare explicit profiling, implicit profiling, and using legacy data:
- Explicit profiling asks visitor to fill out information, questionnaires or leaving comments. Here we let customers tell the site directly what they want. By asking the visitor to specify profile information, such as, the category of music he likes, favourite artists and shows he went, etc.
- Implicit profiling tracks thevisitor behaviour. As Browsing and buying pattern are the behaviours most often assessed. The browsing pattern is usually tracked by saving specific visitor identification and behaviour information. The buying pattern is generally available in the customer purchase database. For example we can check all the things bought by each customer and from it we will be able know his tastes and the average ofalbums bought by him per month.
- Using legacy data accesses legacy data for valuable profile information, such as credit applications and previous purchases. For existing customer and known visitor, legacy data often provides the richest source of profile information.

Filtering – filtering techniques employ algorithms to analyse meta data and drive presentation and recommendations. Thetechniques we can use for this specific website are:
- Content-based filtering – works by analysing the content of the albums to form a representation of the the visitor interests. For the website we could check albums content, in this case the music type based on keywords and every time a visitor gets an album with these keywords that we would set for a group of albums with same style, we record thiskeyword on user's profile and then we can recommend the visitor albums with same keyword, as well as checking user's age before he buy an album with strong words.
- Collaborative filtering – we can use this technique to collects visitor opinion on set of albums, using ratings. So for this technique we can ask visitors to rate an album, and then we can recommend albums to them by checking albumwith similar rates, or albums with same author or similar title.

It is also a nice idea to implement a PersonalBook for each user. The PersonalBook would be tied to the user's profile, and it stores personal information about the user.
Once the PersonalBook is displayed, it provides several information and task-oriented tools, which can be the list of all albums the user has bought. If he...