def _recommendations(model_id, num_interactions, repos, coo, result, name):
"""
:param repos: List of repository IDs to generate recommendations for.
:param coo: Co-occurrence matrix A'A where A is a user x item matrix.
:param result: Function taking two repository IDs and returns a score.
:param name: Name of CSV file to save recommendation results to.
"""
results = []
for idx, id1 in enumerate(repos):
scores = coo[id1][coo[id1] >= 5].index.map(
lambda id2: [model_id] + result(coo, num_interactions, id1, id2))
scores = sorted(scores, key=lambda x: x[3], reverse=True)
# Exclude first result (since it will be id1)
results += scores[1:101]
if idx > 0 and idx % 100 == 0:
log.debug("Finished {}".format(idx))
save_csv(name, results)