A method comprising: generating a plurality of machine-learning scores, each reflecting the extent to which recommending a particular video to a particular user will satisfy a viewing interest, wherein the generating comprises feeding an input feed to a machine learning engine for each video (202, 204, 206) in a set of candidate videos; wherein the input feed for a particular video in the set of candidate videos includes at least: collaborative filtering inputs, comprising: one or more raw collaborative filtering features, each relating to viewing behavior of video consumers; and a collaborative filtering score that was generated based on the one or more raw collaborative filtering features, wherein the collaborative filtering score is a prediction of the degree to which providing the particular video to the particular user will satisfy a viewing interest; content-based filtering inputs, comprising: one or more raw content-based filtering features including features related to the particular video and a compared video; and a content-based filtering score that was generated based on the one or more raw content-based filtering features, wherein the content-based filtering score is a prediction of how similar the particular video is to the compared video; when generating the ML score for the particular video, the machine learning engine determines a weighted combination of the collaborative filtering score and the content-based filtering score, wherein a first relative weight given to the collaborative filtering score corresponds to a first signal strength determined from the one or more raw collaborative filtering features, and wherein a second relative weight given to the content-based filtering score corresponds a second signal strength determined from the one or more raw content-based filtering features; selecting one or more videos (204, 206), from the set of candidate videos, to recommend to the particular user based, at least in part, on the plurality of ML scores; wherein the method is performed by one or more computing devices. generating a plurality of machine-learning scores, each reflecting the extent to which recommending a particular video to a particular user will satisfy a viewing interest, wherein the generating comprises feeding an input feed to a machine learning engine for each video (202, 204, 206) in a set of candidate videos; wherein the input feed for a particular video in the set of candidate videos includes at least: collaborative filtering inputs, comprising: one or more raw collaborative filtering features, each relating to viewing behavior of video consumers; and a collaborative filtering score that was generated based on the one or more raw collaborative filtering features, wherein the collaborative filtering score is a prediction of the degree to which providing the particular video to the particular user will satisfy a viewing interest; content-based filtering inputs, comprising: one or more raw content-based filtering features including features related to the particular video and a compared video; and a content-based filtering score that was generated based on the one or more raw content-based filtering features, wherein the content-based filtering score is a prediction of how similar the particular video is to the compared video; collaborative filtering inputs, comprising: one or more raw collaborative filtering features, each relating to viewing behavior of video consumers; and a collaborative filtering score that was generated based on the one or more raw collaborative filtering features, wherein the collaborative filtering score is a prediction of the degree to which providing the particular video to the particular user will satisfy a viewing interest; one or more raw collaborative filtering features, each relating to viewing behavior of video consumers; and a collaborative filtering score that was generated based on the one or more raw collaborative filtering features, wherein the collaborative filtering score is a prediction of the degree to which providing the particular video to the particular user will satisfy a viewing interest; content-based filtering inputs, comprising: one or more raw content-based filtering features including features related to the particular video and a compared video; and a content-based filtering score that was generated based on the one or more raw content-based filtering features, wherein the content-based filtering score is a prediction of how similar the particular video is to the compared video; one or more raw content-based filtering features including features related to the particular video and a compared video; and a content-based filtering score that was generated based on the one or more raw content-based filtering features, wherein the content-based filtering score is a prediction of how similar the particular video is to the compared video; when generating the ML score for the particular video, the machine learning engine determines a weighted combination of the collaborative filtering score and the content-based filtering score, wherein a first relative weight given to the collaborative filtering score corresponds to a first signal strength determined from the one or more raw collaborative filtering features, and wherein a second relative weight given to the content-based filtering score corresponds a second signal strength determined from the one or more raw content-based filtering features; selecting one or more videos (204, 206), from the set of candidate videos, to recommend to the particular user based, at least in part, on the plurality of ML scores; wherein the method is performed by one or more computing devices.