论文分享——recommender system survey
作者：互联网
- How to evaluate RS?
- How does RS evolve?
- What’s the taxonomy?
- RSs’development trend.
| Internal Functions for RS | Similarity measurement |
|---|---|
| content-based | recommend items similar to items that a user has bought, visited, heard, viewed and ranked positively |
| Demographic filtering | individuals with certain common personal attributes (sex,age, country, etc.) will also have common preferences |
| Collaborative Filtering | make recommendations to each user based on information provided by those users we consider to have the most in common with them |
| Hybrid filtering |
| recommendation methods | description |
|---|---|
| memory-based | usually use similarity metrics to obtain the distance between two users, or two items, based on each of their ratios |
| model-based | Use RS information to create a model that generates the recommendations |
The main purpose of both memory-based and model-based approaches is to get the most accurate predictions in the tastes of users.
Use dimension-reduction to address data-sparsity problem. e.g. combine LSI and SVD
SVD is expensive so it can only be used offline.
| Cold Start | Method |
|---|---|
| New community | encourge users to make ratings |
| New item | have a set of motivated users who are responsible for rating each new item in the system |
| New user |
The item to item version of the kNN algorithm significantly reduces the scalability problem.
| Similarity Measure | |
|---|---|
| KNN | item-to-item, user-to-user |
| singularity-similarity | singular similarity should be awarded a higher value |
| RS-tailored SM | superior compared with traditional SM from statistics |
| JMSD | besides using the numerical information from the ratings (via mean squared differences) also uses the non-numerical information provided by the arrangement of these |
| Heuristic SM | PIP(Proximity–Impact–Popularity) Fig.1 |
| Evaluation of RS results | description |
|---|---|
| Quality of the predictions | MAE, RMSE to compare the ratings and predictions |
| Quality of the set of rcmd | precision, recall F1 |
| Stability | A RS is stable if the predicitions it provides do not change strongly over a short period of time |
| Reliability | how certain the user would like the item |
标签：information,recommender,users,RS,system,item,survey,user,based 来源： https://blog.csdn.net/chendh1028/article/details/111656167