Recommending magazines to users of a digital magazine server

A computer-implemented method for recommending content items to a user of a digital magazine server, the method comprising:

• identifying a specific type of content item for recommending to the user of the digital magazine server;
• determining a time window corresponding to the identified specific type of content item, the duration of the time window determined based on a number of content items having the specific type included in a content store of the digital magazine server, different types of content item corresponding to time windows having different durations;
• identifying a plurality of content items with which the user of the digital magazine server previously interacted during the determined time window corresponding to the identified specific type of content item;
• generating a vector for each content item based at least in part on one or more topics determined from key terms included in a content item, the vector having one or more dimensions with a value of a dimension based at least in part on a number of times a topic occurs in the content item and the topics determined based on one or more characteristics describing frequency of presentation of each of the identified key terms in the content item and describing differences in presentation of each of the identified key terms by the content item relative to presentation of other terms in each content item;
• generating one or more clusters based at least in part on the generated vectors, each cluster including one or more content items;
• determining characteristic vectors for each cluster based at least in part on the generated vectors, a characteristic vector for a cluster based at least in part on one or more of the generated vectors included in the cluster;
• retrieving a plurality of candidate content items each having the identified specific type of content item;
• and
• selecting one or more candidate content items for presentation to the user based at least in part on a measure of similarity between each candidate content item and one or more of the characteristic vectors.

A computer-implemented method for recommending content items to a user of a digital magazine server, the method comprising:

• identifying a specific type of content item for recommending to the user of the digital magazine server;
• determining a time window corresponding to the identified specific type of content item, the duration of the time window determined based on a number of content items having the specific type included in a content store of the digital magazine server, different types of content item corresponding to time windows having different durations;
• identifying a plurality of content items with which the user of the digital magazine server previously interacted during the determined time window corresponding to the identified specific type of content item;
• identifying key terms from each of the plurality of content items;
• generating one or more topics associated with each content item based at least in part on the identified key terms and characteristics describing frequency of presentation of each of the identified key terms in each content item and describing differences in presentation of each of the identified key terms by each content item relative to presentation of other terms in each content item;
• generating a representation of each content item based at least in part on one or more topics associated with a content item, the representation of the content item having one or more dimensions with a value of a dimension based at least in part on a number of times a topic occurs in the content item;
• generating one or more clusters each including one or more content items based at least in part on the generated representations;
• determining characteristic representations for each cluster based at least in part on the generated representation, a characteristic representation for a cluster based at least in part on one or more of the generated representations included in the cluster;
• retrieving a candidate content item having the specific type of content item;
• determining a measure of similarity between the candidate content item and one or more of the characteristic representations;
• and
• selecting the candidate content item for recommendation to the user based at least in part on the determined measures of similarity.