Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show readers how recommender systems work and, more importantly, how to create and apply them for their site. This hands-on guide covers scaling problems and other issues they may encounter as their site grows.
Entry point: The item that the user chooses as a starting point can be considered a strongly positive preference, since the user is looking for something similar to it. Ending point: The final selection or buying decision can also be considered a positive rating. Tweaking: When a user critiques a returned item and moves on to something different, we can consider this a negative rating. Browsing: If the user navigates to other items in the returned set, we can consider this a weak negative rating: if the user truly liked the item he or she would probably not browse further. These heuristics are somewhat weak, since we sometimes find users who are exploring the system to see what it can do, applying tweaks not to get a specific recommendation, but to see what will come back.
Nowadays we hear very often the words "Recommender systems" and mainly it's because they are quite often used by companies for different purposes, such as to increase sales (items' suggestion while purchasing Amazon: user that have bought this as also bought this) or in suggestions to customers to give them a better customer experience (film suggestion Netflix) or also in advertising to target the right people based on preferences similarities. The recommender systems are basically systems that can recommend things to people based on what everybody else did. Here there is an example of film suggestion taken from an online course. I want to thank Frank Kane for this very useful course on Data Science and Machine Learning with Python. Here there is the course's link in case you would like to go deeper with Data Science.
A recent hot topic in recommender systems today that published work from academic researches have appeared in recent years is to incorporate user context is the "context-aware recommender system". There are more published work in this area but I've come across the following which may be useful to readers here: Towards time-dependant recommendation based on implicit feedback. Context-aware preference model based on a study of difference between real and supposed situation data.