r not (2) discover pol associations by anal图 to features related with that POl, and (3)keywords that to special words found within the name and description contained within selected groups of items and (3)search for item, by using text mining techniqu abnormalities between viewed versus used item 2) Likelihood Matrix 6) User Explicit Knowledge Retrieval The likelihood matrix is responsible for linking the user se to the with each one of the categories created in the Pol taxonomy, explicitly provide the system, as well as how should that are concerned. It ranges from-I to 1, where -l means total unlikelihood and 1 represents complete interest. This to the creation of creative ways to ask the user for information. Some approaches include: (I)ask only a certain number of nechanism is the basis for the stereotype module and thus items, and infer the other; (2)ask some information only it is both components work together in order to provide an over- technically needed and (3)shrink user data into shorter confident representation of user interests. Although one versions and then infer the complete data space. Table I shows technique is based on the other, their abstraction level is how, in the devised system, this problem is tackled: different, therefore triggering different results by both sed approach coherently represents TABLE L. USER MODEL COMPONENT ACQUISITION TECHNIQUES both user likes and dislikes, by maintaining a negative and positive action space in which assumptions can diverge within Acquisition technique I Personal, Demographics, Handicaps For Psychology Test,Form Our stereotype system can be easily explained though a set Interests, Current Trip. Past Trips Inferred, Form of development guidelines which originated it. First of all, the Image Association,Form POI taxonomy was re-conceptualized into hierarchical terms er serve a ne basis for the stereotype C. User Customization -Recommender System construction;then, an initial set of stereotypes was created. which will form the comparison basis for that stereotype to be most important step regarding e of the UM process is the each of them being fully described using the previous terms The user customization stage In this inked to a user. Finally, mechanisms were created to stage, the system modifies itself in order to contemplate user's compensate for an eventual insufficiency that might describe objectives within the respective system, therefore acting as the the initial set of stereotypes, as well as the suitability of their ultimate feature in this kind of applications. Up until the terms, namely: (1)propose underused stereotypes for removal (2)propose underused stereotype conditions for removal;(3) component only, which is the RS. Fig. 4 presents al propose overused conditions not included reotypes and techniques contemplated in the devised rS (4)propose new stereotypes based on user profiles(eliminates the grey sheep individual issue) t 4) Psychological Model The user psychological model will be in constant evolution, as the user interacts with the system an it traces of his personality evolution. The features selected to be part of this eu5 odule were based on psychological models devised along the ears, such as [7]. They range from 0 to 1, representing the two extremes of that feature. The user psychological profile Figure 4. Recommender System Techniques evolves by comparing used POI with the cur feeding and evolving the user behavioral model towards the Table I relates the presented techniques with the classical comparison o object(the POI class), adjusting it and changing literature approaches. The system performs a commitment into the input of all components depending on it, such as the RS balancing(1) the acceptance of traditional techniques and(2) introducing innovations into each one of them and also 5) Keywords proposing a new one [12][13[16[17] Tags are extremely user-dependent [10]. If no suitable feedback is shown by users in evolving their use and TABLE IL. COMPARISON BETWEEN LITERATURE AND PROPOSED SYSTEM usefulness, the fact is that tags become less important. With that said, several approaches for initially inserted tags within Literature Technique the items were devised. By automatically gifting items with tags, one of the few downsides of this kind of social media, the cold-start problem, can be diminished. The following types Keywords Content-based filtering to the poi class which classifies the POl; (2)keywords that Stereotypes and Psychologi Psychological Filteringtechnique, naturally grouping related POI, physically related or not; (2) discover POI associations, by analyzing patterns contained within selected groups of items and (3) search for abnormalities between viewed versus used items. 2) Likelihood Matrix The likelihood matrix is responsible for linking the user with each one of the categories created in the POI taxonomy, being classified as a linear model in what literature techniques are concerned. It ranges from -1 to 1, where -1 means total unlikelihood and 1 represents complete interest. This mechanism is the basis for the stereotype module and thus both components work together in order to provide an over￾confident representation of user interests. Although one technique is based on the other, their abstraction level is different, therefore triggering different results by both components. The proposed approach coherently represents both user likes and dislikes, by maintaining a negative and positive action space in which assumptions can diverge within. 3) Stereotypes Our stereotype system can be easily explained though a set of development guidelines which originated it. First of all, the POI taxonomy was re-conceptualized into hierarchical terms which would better serve as the basis for the stereotype construction; then, an initial set of stereotypes was created, each of them being fully described using the previous terms which will form the comparison basis for that stereotype to be linked to a user. Finally, mechanisms were created to compensate for an eventual insufficiency that might describe the initial set of stereotypes, as well as the suitability of their terms, namely: (1) propose underused stereotypes for removal; (2) propose underused stereotype conditions for removal; (3) propose overused conditions not included in stereotypes and (4) propose new stereotypes based on user profiles (eliminates the grey sheep individual issue). 4) Psychological Model The user psychological model will be in constant evolution, as the user interacts with the system and gives it traces of his personality evolution. The features selected to be part of this module were based on psychological models devised along the years, such as [7]. They range from 0 to 1, representing the two extremes of that feature. The user psychological profile evolves by comparing used POI with the current profile, feeding and evolving the user behavioral model towards the comparison o object (the POI class), adjusting it and changing the input of all components depending on it, such as the RS. 5) Keywords Tags are extremely user-dependent [10]. If no suitable feedback is shown by users in evolving their use and usefulness, the fact is that tags become less important. With that said, several approaches for initially inserted tags within the items were devised. By automatically gifting items with tags, one of the few downsides of this kind of social media, the cold-start problem, can be diminished. The following types of tags are automatically set for POI: (1) keywords that relate to the POI class which classifies the POI; (2) keywords that pertain to features related with that POI, and (3) keywords that pertain to special words found within the name and description of the item, by using text mining techniques. 6) User Explicit Knowledge Retrieval This is the UM component most close to the system interface, which deals with what information should the user explicitly provide the system, as well as how should that information retrieval be processed [11]. This issue gives birth to the creation of creative ways to ask the user for information. Some approaches include: (1) ask only a certain number of items, and infer the other; (2) ask some information only it is technically needed and (3) shrink user data into shorter versions and then infer the complete data space. Table 1 shows how, in the devised system, this problem is tackled: TABLE I. USER MODEL COMPONENT ACQUISITION TECHNIQUES User Model Component Acquisition Technique Personal, Demographics, Handicaps Form Psychologics Psychology Test, Form Interests, Current Trip, Past Trips Inferred, Form Stereotypes Image Association, Form C. User Customization - Recommender System The user customization stage of the UM process is the most important step regarding the user point of view. In this stage, the system modifies itself in order to contemplate user’s objectives within the respective system, therefore acting as the ultimate feature in this kind of applications. Up until the current state of the project, this stage encompasses one component only, which is the RS. Fig. 4 presents all techniques contemplated in the devised RS. Figure 4. Recommender System Techniques Table 1 relates the presented techniques with the classical literature approaches. The system performs a commitment into balancing (1) the acceptance of traditional techniques and (2) introducing innovations into each one of them and also proposing a new one [12][13][16][17]. TABLE II. COMPARISON BETWEEN LITERATURE AND PROPOSED SYSTEM System Technique Literature Technique Likelihood Matrix Knowledge-based Filtering Keywords Content-based Filtering Socialization Collaborative Filtering Stereotypes and Psychological Model Psychological Filtering 621