Non-personalized Recommenders

Gorse recommender system offers two non-personalized recommenders: the latest recommender and the popular recommender. However, they are not flexible enough. To improve the flexibility of non-personalized recommendations, Gorse provides a new implementation of non-personalized recommenders.

Configuration

Non-personalized recommenders will traverse each item and its feedback, using expressions provided by the user to filtering and scoring. The configuration of a non-personalized recommender consists of three fields:

• name is the name of the recommender.
• score is the scoring function, represented in Expr language, requiring output typed floating point or integer. Items are sorted in descending order.
• filter is the filtering function, represented in Expr language, requiring output typed boolean.

Tips

If you want to sort items in ascending order, you need to add a negative sign yourself.

In expressions, the item variable represents the current item while feedback variable represents positive feedback to the current item.

The type of item is the Item structure, and the syntax for accessing fields is consistent with Go language. For example, item.Timestamp will return the timestamp of the item.

type Item struct {
    ItemId     string
    IsHidden   bool
    Categories []string
    Timestamp  time.Time
    Labels     any
    Comment    string
}

The type of feedback is []Feedback, a slice of Feedback. The syntax for accessing the array is consistent with the Go language. For example, feedback[0].FeedbackKey.FeedbackType will return the feedback type of the first positive feedback.

type Feedback struct {
    FeedbackType string
    UserId       string
    ItemId       string
    Timestamp   time.Time
    Comment     string
}

Expr language provides a lot of built-in functions that are sufficient to write more complex calculation logic. In addition to built-in functions that handle single variables such as mathematical operators, comparison operators, logical operators, etc., it also provides many array-related built-in functions. To learn more about syntax please visit Expr's official documentation.

API

You can access non-personalized recommendations through the following API:

curl http://localhost:8087/api/non-personalized/<name>

Examples

Latest Items

You can sort the latest items directly by using timestamps.

[[recommend.non-personalized]]
name = "latest"
score = "item.Timestamp.Unix()"

Since item.Timestamp is a time.Time struct and cannot be used directly for sorting, it needs to call the Unix() method to convert it into int64 type.

Most Liked Items Updated within the Last Week

Use filter to exclude items from more than a week ago, and calculate the number of likes as the sorting score.

[[recommend.non-personalized]]
name = "most_liked_weekly"
score = "count(feedback, .FeedbackType == 'like')"
filter = "(now() - item.Timestamp).Hours() < 24 * 7"

The count function will calculate the length of the feedback slice, the second parameter indicates that it only counts when the feedback type is 'like'. The now() function gets the current time, subtracts item time and requires that the number of hours be within a week (24*7 hours).

Hacker News

It is said that Hacker News uses the following formula to calculate scores for content ranking:

$$\frac{p-1}{(T+2)^G}$$

where $p$ is the number of upvotes for the content, $T$ is the time from posting to score calculation, and $G$ is the gravity value used to control the rate at which content popularity decays over time. If you set $G$ to 0.5, you can add the following configuration item:

[[recommend.non-personalized]]
name = "trending"
score = "(count(feedback, .FeedbackType == 'upvote')-1)/((now() - item.Timestamp).Seconds()+2)^0.5"