A Fortunate Refining Trip Recommendation Model

Abstract

Personalized travel recommendations propose locations of interest (LOIs) for users. The LOI sequence suggestion is more complicated than a single LOI recommendation. Only a few studies have considered LOI sequence recommendations. Creating a reliable succession of LOIs is difficult. The two LOIs that follow each other should not be identical or from the same category. It is vital to examine the types of subsequent LOIs when designing a sequence of LOIs. Another issue is that providing precise and accurate location recommendations bores users. It can be tedious and monotonous to look at the same types of LOIs repeatedly. Users may want to change their plans in the middle of a trip. The trip must be dynamic rather than static. To address these concerns in the recommendations, organize a customized journey by looking for continuity, implications, innovation, and surprising (i.e., high levels of amusement) LOIs. We use LOI-likeness and category differences between subsequent LOIs to build sequential LOIs. In our travel recommendations, we leveraged luck and dynamicity. We suggest a fortunate refining trip recommendation (FRTR) to address the issues of identifying and rating user pleasure. An algorithm oof compelling recommendation should offer what we are likely to enjoy and provide spontaneous yet objective components to maintain an open doorway to new worlds and discoveries. In addition, two advanced novel estimations are presented to examine the recommended precision of a sequence of LOIs: regulated precision (RP) and pattern precision (PP). They consider the consistency and order of the LOIs. We tested our strategy using data from a real-world dataset and user journey records from Foursquare dataset. We show that our system outperforms other recommendation algorithms to meet the travel interests of users.

Content and Other Resources Recommendations for Individuals with Intellectual Disability: A Review

“…The exploration-exploitation dilemma indicates how the system has to choose between recommending an item that allows it to learn more (explore) about the user or an item that keeps the user interested (exploit). Finding the delicate balance between exploration (i.e., recommending new/"fresh" or "unexpected" [69] items) and exploitation (i.e., proposing well-established items) is a great challenge [70] and a common trade-off in recommendation systems [41]. In a system that has a very dynamic corpus of items, i.e., a very large or rapidly changing set of items, the recommendation approach should be responsive enough to model newly uploaded content and occasionally make more "exotic" recommendations [28].…”